RU2821143C1 - Wearable device, method and system for measuring blood parameters - Google Patents

Abstract

FIELD: medicine.

SUBSTANCE: group of inventions relates to a wearable device, a system and a method for determining at least one blood parameter of a user. Sets of characteristics of the molecular structure of blood are extracted using at least one photoplethysmographic (PPG) sensor and the cell structure of blood using a bioimpedance (BIA) sensor. Thereafter, a set of characteristics of the blood structure is formed, which determine the state of the blood of the user, and determining the values of at least one blood parameter of the user based on the obtained set of characteristics of the molecular and cellular structure of blood and information from the pre-compiled database.

EFFECT: invention provides non-invasive instant or continuous monitoring of various blood parameters, such as concentration of haemoglobin (cHb), total weight of haemoglobin (tHb), haematocrit (HCT), total erythrocyte volume (tEV), plasma volume (PV) including at edge values of controlled parameters.

48 cl, 24 dwg, 3 tbl, 1 ex

Description

Technical field

[0001] The present invention relates to devices and systems for non-invasive instant or continuous personal monitoring of human health, in particular, determination of blood parameters such as hemoglobin concentration (cHb), total hemoglobin mass (tHb), hematocrit index (HCT), total erythrocyte volume (tEV), mean erythrocyte hemoglobin concentration (MCHC), plasma volume (PV), including at extreme parameter values, using wearable devices such as modern smart watches and fitness bracelets.

In addition, the invention relates to a method for determining blood parameters implemented in wearable devices.

BACKGROUND OF THE INVENTION

[0002] Blood parameters are markers of a person’s health status and their timely monitoring makes it possible to prevent, timely detect and control the course of the disease, as well as other disorders in the functioning of the body. Therefore, continuous (constant) monitoring of blood parameters is necessary, of which the main ones are hemoglobin concentration, hematocrit, total erythrocyte volume, average hemoglobin concentration in erythrocytes, blood volume, plasma volume, etc.

[0003] Below are the conventions of some basic blood parameters used in the present description:

[0004] RBC - red blood cells (erythrocytes). These are the formed elements of blood containing hemoglobin. Red blood cells are highly specialized blood cells whose main function is to participate in gas exchange due to the ability of red blood cells to bind oxygen and carbon dioxide and transport them in the bloodstream. Oxygen binding is ensured by the high content of hemoglobin in erythrocytes. Red blood cells are involved in hemostasis, maintaining acid-base balance, immune reactions and represent the largest population of blood cells. Under physiological conditions, in men the number of circulating red blood cells is 4.1-5.2 × 10 ¹² cells per liter, in women - 3.6-4.6 × 10 ¹² cells per liter. The concentration of red blood cells can increase due to loss of fluid through sweat, vomiting, diarrhea, that is, when the blood thickens. A decrease in the indicator is usually a sign of anemia, most often iron deficiency.

[0005] Hb - hemoglobin (hemoglobin concentration in whole blood). This is the main respiratory pigment and the main component of red blood cells, which performs important functions in the human body: transferring inhaled oxygen from the lungs to biological tissues and organs, and carbon dioxide from tissues and organs to the lungs, where it is exhaled. This occurs due to the ability of oxygen to reversibly bind with iron, the atoms of which are “embedded” in hemoglobin. When interacting with biological tissues and organs, red blood cells are “freed” from oxygen and “take up” carbon dioxide. Hemoglobin also plays a significant role in maintaining the acid-base balance of the blood. The buffer system created by hemoglobin helps maintain blood pH within certain limits. It is hemoglobin, or rather its iron, that colors red blood cells red.

[0006] NCT - hematocrit (hematocrit number, hematocrit indicator). This is the ratio of the volume of formed elements and blood plasma. It is believed that hematocrit reflects the ratio of the volume of erythrocytes and the volume of blood plasma, since the volume of blood cells consists mainly of erythrocytes. Blood consists of 40-45% formed elements (erythrocytes, platelets, leukocytes) and 60-55% plasma. Hematocrit depends on the number of RBCs and the MCV value (mean erythrocyte volume) and corresponds to the product RBC×MCV. Normal hematocrit is 40-50% in men and 35-44% in women.

[0007] The value of NCT is widely used to judge the degree of anemia, in which, as a rule, a decrease in hematocrit of up to 20-25% is detected, which in anemia occurs in parallel with a decrease in the number of red blood cells. NBT reflects only the volume of red blood cells in the blood, and not their total mass in the body. Thus, in patients with shock due to hemoconcentration, NBT may be normal or even high, although the total mass of red blood cells is significantly reduced due to blood loss. Therefore, NST cannot be a reliable parameter for assessing the degree of anemia immediately after blood loss or blood transfusion.

[0008] An increase in NCT to 55-65% and higher is characteristic of polycythemia vera; a less sharp increase to 50-55% is observed with symptomatic erythrocytosis. With overhydration (hydremia), the plasma volume increases, and the volume of erythrocytes (in percentage) decreases, which means the NBT decreases.

[0009] The ratio between the volumes of erythrocytes and plasma changes during dehydration (toxicosis, diarrhea, vomiting), when the blood thickens, i.e. the plasma volume decreases, and the volume of red blood cells increases as a percentage, which means the hematocrit increases.

The value of NBT (in combination with other indicators) is used to assess the degree of acute blood loss, as well as to calculate erythrocyte indices and a number of biochemical indicators.

[0010] PLT is the absolute content of blood platelets (platelets) - the formed elements of blood involved in hemostasis. Platelets are blood elements responsible for the formation of a thrombotic clot during bleeding. The normal PLT value is 178-318×10 ⁹ cells per liter. Exceeding normal values may indicate physical stress, anemia, inflammatory processes, or may indicate more serious problems in the body, including oncological diseases and blood diseases. And a significant decrease in them can be a sign of hematological blood diseases, including leukemia. Also, the value may decrease due to the use of antitumor and cytostatic drugs, hypothyroidism.

[0011] WBC - white blood cells (leukocytes). These are the main defenders of our body, representatives of the cellular immunity. An increase in the total number of leukocytes most often indicates the presence of an inflammatory process, predominantly of a bacterial nature. It may also be a sign of so-called physiological leukocytosis (under the influence of pain, cold, physical activity, stress, during menstruation, sunburn). The normal white blood cell count in men and women is usually 4.5-11.0 x 10 ⁹ cells per liter.

[0012] A decrease in white blood cells is a sign of immune suppression. The cause most often is previous viral infections, taking certain medications (including non-steroidal anti-inflammatory drugs and sulfonamides), weight loss, and much less often - immunodeficiencies and leukemia.

[0013] Red blood cell indices: MCV, MCH, MCHC:

[0014] MCV is the average volume of red blood cells. This value is measured in cubic micrometers or femtoliters (fl) and is normally 81-95 fl for men and 82-98 fl for women. MCV of erythrocytes decreases with iron deficiency anemia and increases with B12 deficiency anemia, hepatitis, and decreased thyroid function.

[0015] MCH is the average hemoglobin content in an individual red blood cell in absolute units. This value, which is normally 26-32 pg, is proportional to the hemoglobin/red blood cell count ratio. This indicator rarely increases, but a decrease is a sign of anemia or decreased thyroid function.

[0016] MCHC - the average concentration of Hb in erythrocytes, which is normally 300-380 g/l, reflects the degree of saturation of the erythrocyte with hemoglobin. A decrease in MSHC is observed in diseases with impaired hemoglobin synthesis. However, this is the most stable hematological indicator. Any inaccuracy associated with the determination of Hb, NCT, MCV leads to an increase in MSHC, therefore this parameter is used as an indicator of instrument error or error made when preparing the sample for research. Unlike MCH, MSHC does not depend on cell volume and is a sensitive test for disorders of hemoglobin formation.

[0017] In addition, blood is characterized by other parameters, such as various platelet indices, leukocyte indices, erythrocyte indices, etc.

[0018] One of the most important blood parameters is hemoglobin, which is a complex protein belonging to the group of hemoproteins, the protein component of which is represented by globin, and the non-protein component by the prosthetic group. The prosthetic group in the hemoglobin molecule is represented by 4 identical iron porphyrin compounds, which are called themes. The heme molecule consists of porphyrin IX linked to iron by two nitrogen atoms covalently and two other nitrogen atoms by coordination bonds. The iron (II) atom is located in the center of the heme and gives the blood its characteristic red color; its oxidation state does not change regardless of the addition or release of oxygen.

[0019] It should be noted that the concentration of Hb in the blood, like all other components, depends on the amount of water in the blood (hydremia). With blood thickening (loss of fluid), a relative increase in Hb concentration may occur, masking its absolute decrease. This leads to errors in determining Hb. For example, in the initial period of acute anemia due to blood loss, the Hb concentration will be relatively satisfactory due to blood thickening caused by fluid loss. In essence, in this case there is severe anemia, since in fact the absolute amount of Hb in the body is significantly reduced.

Thus, when determining Hb, many additional factors must be taken into account, especially the amount of fluid in the body (or water in the blood).

[0020] As discussed above, the most characteristic property of hemoglobin is the reversible addition of gases such as O ₂ , CO ₂ , etc. In the blood, hemoglobin exists in at least four forms: oxyhemoglobin, deoxyhemoglobin, carboxyhemoglobin, methemoglobin. In erythrocytes, the molecular forms of hemoglobin are capable of interconversion; their ratio is determined by the individual characteristics of the organism.

[0021] Normal values for hemoglobin concentration: in men 130-165 g/l, in women 120-150 g/l, in children 110-155 g/l, in pregnant women 110-120 g/l. A diagnostically significant decrease in the lower threshold of normal hemoglobin levels occurs in men in the age group 65-74 years. The hemoglobin concentration increases as the number of red blood cells increases and decreases as the number of red blood cells decreases. A decrease in the number of red blood cells, and therefore a reduced concentration of hemoglobin in the blood (low hemoglobin) may be due to a decrease in the formation of red blood cells in the bone marrow, their loss as a result of bleeding or destruction within the body, unbalanced nutrition, bad habits, impaired hemoglobin synthesis, gastrointestinal diseases tract, chronic kidney disease, liver cirrhosis, myxedema, hemolysis, etc. The consequences of low hemoglobin are disruptions of the immune system, deterioration of the skin, a negative impact on the ability to motherhood, tachycardia, shortness of breath, provoking a stroke and heart attack. In this case, a person is often in a state of anemia, when the body does not receive enough oxygen, which causes weakness, drowsiness and fatigue.

[0022] An increased concentration of Hb indicates polycythemia, hemoconcentration due to dehydration, burns, intestinal obstruction, persistent vomiting, exposure to high altitudes, excessive physical activity; cardiovascular pathology, usually congenital, leading to significant venous discharge; lung diseases leading to decreased pulmonary perfusion, poor lung aeration, pulmonary arterial fistula; chronic chemical exposure to nitrites, sulfonamides, causing the formation of meth- and sulfohemoglobin.

[0023] Related to hemoglobin concentration is blood oxygen saturation (oxygenation or SpO ₂ ), which is the percentage of oxyhemoglobin in the blood, that is, the ratio of the amount of oxyhemoglobin to the total amount of hemoglobin. For example, oxygenation of 95-100% is normal, but may vary if a person has lung disease. Oxygenation <90% is considered low and is called hypoxemia, and the body requires oxygen supplementation. The causes of low blood oxygen saturation are anemia, sleep apnea, smoking, lung diseases (asthma, emphysema, etc.), viral infections (COVID-19, etc.), air quality, reduced cardiac capacity, strong painkillers. The consequences of low oxygenation include headaches, tachycardia, shortness of breath, and damage to the heart and brain. It should be noted that the level of oxygenation reflects the degree of blood saturation with oxyhemoglobin, but does not characterize the total hemoglobin content in the blood.

[0024] Thus, the blood parameters discussed above are important and significant indicators of human health and require continuous monitoring.

[0025] Invasive and non-invasive methods are used to determine blood parameters. Invasive methods are laboratory methods, which include chemical, spectrophotometric, colorimetric methods. For example, to determine hemoglobin, the saponin method, Sali's method, is used, when hemoglobin derivatives formed during its oxidation and the addition of various chemical groups to the heme, leading to a change in the valence of iron and the color of the solution, are analyzed. Chemical and spectrophotometric methods are highly accurate and are recommended as reference methods, but due to the laboriousness and significant cost of analysis, they are not used for routine determinations. For routine laboratory tests, colorimetric methods are most preferred, as they are the cheapest, simplest and fastest to perform.

[0026] However, laboratory methods for assessing blood parameters involve venipuncture or finger pricking, can cause pain and create a potential risk of infection for patients. In addition, in this case, qualified personnel and laboratory equipment are needed to carry out tests, and waste must be disposed of. Moreover, laboratory methods are not suitable for continuous monitoring of blood parameters.

[0027] Today, there are many devices for non-invasive measurement of blood parameters that reduce discomfort for patients. However, they are not always applicable for continuous monitoring of blood parameters.

[0028] Currently, bioimpedance measurement (BIA) is increasingly being used to non-invasively determine hematocrit. As the hematocrit level decreases, a change in the components of the blood impedance and a shift in the frequency response of the impedance to higher frequencies are noted. Using a BIA sensor built into a wearable device, you can estimate the amount of fat, bone, muscle tissue, fluid content in the body, body mass index, etc.

[0029] The advantages of measuring blood parameters using wearable devices, in particular modern smart watches or fitness bracelets, are instant or continuous measurement, ease of use, and the possibility of long-term monitoring of blood parameters. At the same time, the assessment of blood parameters using wearable devices is not accurate enough, especially when measuring the edge values of parameters.

[0030] Non-invasive methods for assessing hemoglobin and blood oxygenation include, for example, pulse oximetry, which provides control of the percentage of hemoglobin saturated with oxygen by assessing tissue for the transmission of optical radiation (by pulse amplitude) and heart rate (HR). To non-invasively determine blood oxygenation, a section of biological tissue containing arterial vessels is placed in the working area of a photoplethysmographic (PPG) sensor. Traditional oximeters usually have the form of a “clothespin” to be placed on the patient’s finger or earlobe, that is, where radiation can be transmitted through an area of tissue, and analyze the radiation passing through the finger or earlobe, that is, they measure an area of biological tissue for transmission. In human tissues, bones, venous blood and arterial blood, radiation is absorbed, reflected and scattered, which, when studying blood flow, is determined by the size of the vessels or the volume of blood passing through the tissue area being examined. The narrowing and expansion of the vessel under the influence of arterial pulsation of blood flow, which is caused primarily by changes in blood flow in the arteries and arterioles, cause a corresponding change in the amplitude of the signal received from the output of the photodetector. The pulse oximetry technique is based on the principles of photoplethysmography. Hemoglobin serves as a kind of filter for light flux, and its “color” and “thickness” can change. The “color” of the filter depends on the percentage of oxyhemoglobin. This is the basis for the ability of a pulse oximeter to determine the degree of blood oxygenation. The change in the “thickness” of the filter is affected by the increase in blood volume in the arteries and arterioles with each pulse wave. After measuring the pulse rate and pulse wave amplitude, the ratio of the degree of absorption of radiation of different wavelengths, for example, IR and red waves, is analyzed using a microprocessor, then the saturation of the pulsating flow of arterial blood with oxygen is calculated.

[0031] In this case, the signal from the output of the sensor, proportional to the absorption of light passing through the tissue, includes two components: a variable (AC) component, due to changes in arterial blood volume with each heartbeat, and a constant (DC) component, determined by the optical properties of the skin, bones, venous blood and other tissues of the area under study, the total absorption of which does not change during the propagation of the pulse wave.

[0032] This is a traditional approach for signal analysis in pulse oximetry, based on separating the DC and AC components of the time series (DC and AC components in the time domain or DC and AC components) of the PPG signal (pulse signal). In this case, the integral area of the signal curve is traditionally measured, and the signal shape is usually not taken into account. At the same time, the DC component is used to normalize the signal, since signal transmission depends not only on the absorption/reflection/scattering coefficients of skin, bones, blood and other tissues that make up the “filter” for the signal, but also on the thickness of the “filter” itself.

[0033] Most known methods describe the theory and operation of pulse oximeters in transmission mode, therefore, in the prior art, such approaches are described based on the relevant laws and equations, in particular, the Bouguer-Lambert-Beer law, which determines the attenuation of a parallel monochromatic beam of light as it propagates in an absorbing environment. However, there is not enough information in the prior art regarding the application of the above method with AC/DC components in a pulse oximeter in reflection mode. It is obvious that operation of the device in reflection mode requires other approaches, including additional analysis of the pulse waveform.

[0034] However, there are certain difficulties in analyzing the AC components of a PPG signal, consisting in the fact that the AC component has a complex shape, which depends on the wavelength and carries information about the spectral properties of the tissue.

[0035] Bioimpedance analysis (BIA) is a contact method for measuring the electrical conductivity of biological tissues, making it possible to assess a wide range of morphological and physiological parameters of the body. Bioimpedance analysis measures the active and reactive resistance of the human body and/or its parts at various frequencies. Based on them, body composition indicators are calculated, such as, for example, the amount of fat, bone, muscle tissue, mass, volume and distribution of fluid in the body, body mass index.

[0036] BIA is based on determining the electrical impedance of biological objects. Impedance is called complex (total) electrical resistance. This quantity has two components: active and reactance. Active, or ohmic, resistance characterizes the ability of biological tissues to thermally dissipate electric current. Reactance is characterized by a phase shift of the current relative to the voltage due to the capacitive properties of cell membranes, which are capable of accumulating electrical charge on their surface. The physical essence of the bioimpedance measurement method is to measure the total electrical resistance of the body using a bioimpedance analyzer. Because different tissues have different resistances, the fluid, fat, bone and muscle tissue content of the body can be accurately measured and determined. In this case, two pairs of electrodes and a probing sinusoidal current of constant frequency and low power (no more than 500-800 μA) are used.

[0037] Electrical current can flow around and through cells. The cell boundaries are formed by membranes, which, in their electrical properties, are capacitors that depend on the frequency of alternating current. The equivalent circuit of a biological object contains the resistance of the extracellular fluid, the resistance of the cellular fluid and the membrane capacitance.

[0038] The specific resistance of biological tissues, determined for a given current frequency, can change significantly under the influence of physiological and pathophysiological factors: kidneys and lungs change electrical conductivity with different blood and air filling, muscle tissue with different degrees of muscle contraction, blood and lymph with changes in the concentration of proteins and electrolytes, foci of damage (compared to normal tissue) - as a result of edema or ischemia of various natures, tumors and other causes. This allows the use of bioimpedansometry to quantitatively assess the state of organs and body systems in various diseases, as well as to identify changes in tissues caused by medicinal, orthostatic, physical and other stresses.

[0039] Body composition BIA is primarily an assessment of the amount of fluid in the body, since it is the fluid medium that creates the active component of conductivity. The volume of fluid in the body is assessed by impedance using physical and/or empirical models (see, for example, Gaivoronsky I.V. et al., Bioimpedansometry as a method for assessing the component composition of the human body (literature review), Vestnik St. Petersburg State University, Medicine, 2017 , volume 12, issue 4).

[0040] Existing devices for non-invasive optical measurement of blood parameters, such as hemoglobin, have fairly high accuracy for users with normal hemoglobin levels, but low accuracy for determining low and high hemoglobin levels, especially for women. At the same time, it is people with low and high hemoglobin levels who need the most frequent hemoglobin monitoring.

[0041] US Pat. No. 10231657 B2 (Masimo Corporation) discloses a total hemoglobin screening sensor. The total hemoglobin index system displays the total hemoglobin mass (tHb) value using an index LED emitter. A patient fails a tHb index test if tHb measurements are trending downward and/or tHb values fall below a predetermined index threshold. If the patient fails the tHb index test, the high-resolution sensor displays a specific tHb measurement value using a set of high-resolution LEDs. Moreover, the number of high-resolution LEDs in such a set is greater than the number of LEDs in the index set.

The disadvantages of this solution are: the use of only a PPG sensor in a transmission configuration, the lack of taking into account the cellular structure of the blood (fluid content, body composition), in addition, the device is not wearable.

[0042] Patent application US 2011/0080181 A1 (Tanita Seisakusho KK) discloses a device for biometric measurements. The device measures the impedance of various parts of the body and estimates the hematocrit by analyzing the impedance differences at two different frequencies and the detected pulse rate.

The disadvantages of this solution are: conducting only bioimpedance analysis, assessing only hematocrit, lack of accounting for water and the molecular structure of blood, high frequency (more than 10 MHz) and the need to detect the pulse to adjust the frequency.

[0043] Patent application US 2015/0260705 A1 (Kwangju Institute of Science & Technology) discloses a method and apparatus for measuring blood hematocrit. The device measures the impedance of a body part at two points along the bloodstream and estimates the hematocrit of the flowing blood by analyzing the impedance differences at these two points.

The disadvantages of this solution are: conducting only bioimpedance analysis, assessing only hematocrit, lack of accounting for water and the molecular structure of blood, the need to detect blood flow, and specific placement of electrodes.

[0044] Patent application US 2022/0265178 A1 (Bao Tran) discloses a smartwatch designed to detect user activity and adjust glucose assessment accordingly.

The disadvantages of this solution are: the presence of many PPG and BIA sensors, only recognition of activity and the nature of activity, lack of specific accounting of water or blood structure, lack of determination of blood parameters.

[0045] Patent application US 2019/0209056 A1 (Lo Chien-Ming) discloses a method and apparatus for optical detection of Kawasaki disease and treatment outcome. This involves analyzing the optical signal at three different wavelengths, assessing and comparing the signal characteristics and hemoglobin levels in the test subject and healthy patients.

The disadvantages of this solution are: the use of only the PPG signal, focusing only on disease detection, insufficient consideration of fluid, lack of consideration of the cellular structure of the blood, and therefore poor efficiency in determining edge values.

[0046] Patent application US 2021/0000397 A1 (Robert Ricky Steuer) discloses a method and apparatus for non-invasive photometric diagnosis of blood constituents. In this case, a method and device are disclosed that use only one wavelength (800 nm, isosbestic) or add two more wavelengths to determine photo-optical parameters in vivo and calculate blood parameters such as NBT, Hb, PV, etc.

The disadvantages of this solution are: the use of only the PPG signal, the use of 1 to 3 wavelengths, insufficient consideration of water, lack of consideration of the cellular structure of the blood, and therefore poor efficiency in determining edge values.

[0047] Recent publications such as WO 2023/003980 A1 and WO 2023/287789 A1 (Masimo Corporation) disclose wearable devices in the form of smart watches for monitoring physiological parameters of the user, which can be placed on the wrist, with groups of sources and detectors radiation are located on one side of the devices. At the same time, when describing the physical principles of operation of the PPG sensor, the authors of the publications refer to the Bouguer-Lambert-Beer law, as noted above in relation to other solutions of the prior art. However, when working in reflection mode, this approach does not work and gives incorrect results. In addition, signal processing is performed using traditional pulse oximetry signal processing methods, i.e. These publications focus on the design of the sensor, without describing specific signal processing methods, especially in relation to the measurement of specific blood components, such as hemoglobin. In standard mode, when the sensor is worn on the wrist, it is usually possible to measure heart rate, heart rate variability and blood oxygenation. To measure specific parameters, an external sensor can be used, connected to the watch port and placed, for example, on the user’s finger. Thus, these publications do not disclose the features of processing signals received in reflection mode, which make it possible to measure specific blood parameters, such as hemoglobin concentration. Those. these publications do not contain information about the shapes of the PPG signal at different wavelengths, the differences in the optical paths of radiation at these wavelengths, the differences in the types of vessels and tissues with which radiation of different wavelengths predominantly interacts and, accordingly, about the method of taking into account differences in optical paths and types of vessels and tissues through the shape of the PPG signal and a method for describing the shape of this signal through the amplitude-phase characteristics of harmonics.

The disadvantages of this solution are: the use of only the PPG signal, the lack of taking into account the fluid in the body and the cellular structure of the blood, and therefore poor efficiency when measuring edge values.

[0048] Thus, the main problem in the art is the lack of a method and easy-to-use wearable device for instantaneous or continuous monitoring of blood parameters such as hemoglobin, hematocrit, red blood cell volume, plasma volume, blood volume, etc., in which corrects the determined values of blood parameters based on the molecular and cellular structure of blood.

[0049] Therefore, there is a need for a wearable device and method for determining blood parameters that provides accurate measurement even of edge values and is suitable for non-professional use. In other words, a device is required for non-invasive, unobtrusive, instantaneous or continuous monitoring of various blood parameters.

[0050] Possible products that use the method of the invention are wearable electronic devices such as smart watches or smart bracelets, stationary diagnostic devices, household appliances, and personal health monitoring gadgets.

The essence of the invention

[0051] In view of the technical problems set forth above, the present invention will now be described by way of example and not by way of limitation with reference to the description and drawings below.

[0052] This summary of the invention precedes the detailed description of specific exemplary embodiments in order to provide a general understanding of aspects of the claimed invention that will be further explained below, and is in no way intended to define or limit the scope of the present invention.

[0053] The objective of the present invention is to create wearable devices with the function of determining the user's blood parameters based on data collected from the sensors of the wearable devices. In particular, PPG and BIA sensors are used in the present invention. The principles of bioimpedance measurement, the electronic devices used for this, electrical circuits including current-voltage circuit configuration modules, current-voltage measurement modules, BIA sensors are described in detail in the publications of international patent applications WO2020/138667 A1 (“Electronic device configured to compensate for error in bioimpedance value" (Electronic device designed to compensate for errors in bioimpedance value)) dated 07/02/2020 and WO2022/265446 A1 (“Device and method for human body impedance analysis insensitive to high contact impedance and parasitic effects” (Device and method for analyzing the impedance of the human body, insensitive to high contact impedance and parasitic effects)) dated 12/22/2022 by the same applicant as this application, the contents of which are incorporated herein by reference in their entirety.

[0054] Within the scope of the present invention, an extensive study of the correlation between electrical (extracted from recorded BIA signals) and optical (extracted from PPG signals) characteristics and blood parameters was carried out. The solution to the problem of predicting blood parameters taking into account the molecular and cellular structure of the user's blood was based on identifying empirical patterns in the training data placed in a common database using a machine method. The database can be constantly updated, and, in addition, the predictive model (forecasting algorithm) is refined through machine learning. In the present invention, a prediction model is created for a wrist-worn device, since the training of the prediction model was based on data captured by sensors of the wrist-worn device.

[0055] The BIA sensor detects the electrical conductivity of biological tissues, making it possible to assess a wide range of morphological and physiological parameters of the body. Impedance is the complex electrical resistance of a circuit to alternating current. For a biological object, the impedance is of a composite (complex) nature Z=R+i*X, where R is the active ohmic component, X is the reactive capacitive component of the impedance. Impedance is characterized by modulus and phase angle. The area of contact between the electrodes and biological tissue also contributes to the capacitive component of the impedance. Contact impedance (electrode contact with biological tissue) takes into account the physical and chemical processes occurring in the area of contact of electrodes with biological tissue (for example, electrode polarization, skin condition (wet or dry), etc.).

[0056] It is known from the prior art that when a direct electric current passes through an area of muscle tissue, the electrical resistance depends only on the conductivity of the extracellular environment. From the point of view of biophysics, this is easily explained, since cell membranes consisting of phospholipids have very low conductivity. The alternating sinusoidal current also spreads throughout the intracellular space; with increasing frequency, the current density in the extracellular and intracellular spaces tends to level out.

[0057] In the region of low frequencies of alternating current, the impedance practically coincides with the value of the active resistance, and the reactance is close to zero. As the frequency of the current increases, the reactance increases to a certain maximum corresponding to the characteristic frequency fc. With a further increase in frequency, the reactance decreases, and in the limit the impedance will again be equal to the active resistance. When the frequency of the sinusoidal current changes, the angle between the impedance and active resistance vectors changes. This angle is called the phase angle, is calculated as the arctangent of the ratio of reactive and active resistance: ϕ(deg)=(180/π)arctg (X/R) and depends only on the conductivity of the extracellular environment.

[0058] It is known from the prior art that the variable component of the resistance of the human body is determined mainly by the electrical conductivity of the blood, as well as the degree of blood filling of the vessels (Polishchuk V.I., Terekhova A.G. Technique and methodology of rheography and rheoplethysmography, Moscow, Medicine, 1983, 176 p.). The degree of blood filling does not depend on the frequency of the probing current, and since the ratio of the amplitudes of the variable resistance components at frequencies of 20-40 kHz and 200-300 kHz is used, this ratio will be determined by the electrical properties of the blood. Alternating current with a frequency below 40 kHz spreads mainly through vessels; cells are a non-conducting medium for it, since their resistivity is significantly higher than the resistivity of liquid media at these frequencies. Thus, the electrical conductivity of blood is determined by the electrical conductivity of plasma; red blood cells are a non-conducting medium. At frequencies below 20 kHz, the skin resistance increases, above 40 kHz - part of the current passing through the cells, which distorts the result. At frequencies of 200-300 kHz, red blood cells begin to take part in conduction acts (Ivanov G.G., Meshcheryakov G.N., Kravchenko I.R. et al., Bioimpedancemetry in assessing the water sectors of the body, Anesthesiology and Reanimatology, 1999, 3, p. 59). The upper limit of the limitation is determined by the fact that above 300 kHz no changes in electrical conductivity are observed.

[0059] Optical characteristics of biological tissue are obtained using PPG signals at at least two wavelengths. The PPG sensor detects light at the at least two wavelengths to produce a set of detected PPG signals in the time domain. Then, using the processor built into the smartwatch, which is also used for subsequent stages of optical signal processing, a reference PPG signal at a certain wavelength is selected. The reference PPG signal is selected from among all available signals at different wavelengths based on the best signal-to-noise ratio, highest amplitude, and minimum noise (most often a signal at the green light wavelength). The PPG signals are then converted from the time domain to the frequency domain using the Fourier transform or other frequency transforms such as the Hilbert-Huang transform, etc.

[0060] In the frequency domain, the position of the first harmonic (fundamental component) f ₀ is determined from the reference PPG signal and used to analyze signals at other wavelengths. Based on the position of the first harmonic f ₀ , the amplitude-phase characteristics of the harmonics of all received optical signals are determined. Thus, for all combinations of wavelengths of the PPG signal and harmonics, a set of characteristics is obtained which uniquely describes the shape of the pulse wave at each wavelength X.

[0061] Thus, as a result of processing the signals registered by PPG and BIA sensors, a set of characteristics of the molecular and cellular structure (structure) of blood is formed, which is used to predict blood parameters (hemoglobin, hematocrit, total volume of red blood cells, plasma volume, number of red blood cells and etc.). The specified set of characteristics includes at least one parameter of the user's body impedance, for example, the value (Z) of the user's body impedance module, the phase angle (ϕ) of the user's body impedance at certain frequencies (at least at two frequencies from the ranges of 20-40 kHz and 200-300 kHz, respectively), the total amount of water (fluid volume) in the body (TBW), the ratio of the amount of intracellular fluid (ICW) to the amount of extracellular fluid (ECW), the value of the first harmonic (f ₀ ), the value of amplitude-phase characteristics ( t _λ ⁰ ) harmonics (at least the amplitude and total energy of at least two harmonics within a spectral band specified relative to the central frequency, the position of which is determined by the reference frequency corresponding to the heart rate) of at least two PPG signals with different wavelengths.

[0062] By molecular structure we mean the presence and concentration of dye molecules (hemoglobin or other component) in the blood. Cellular structure refers to the spatial distribution of dye molecules in the blood stream. Since the dye (in particular, hemoglobin) is not distributed uniformly throughout the entire tissue volume, but is localized in the structure of certain blood cells (erythrocytes), the nature of the interaction of the dye with the probing radiation (both optical and electrical) depends on both the concentration of the dye and on its spatial distribution. The optical signal of the PPG sensor is predominantly sensitive to the molecular structure of the blood, i.e. dye concentration. In this case, the nature of the backscattered radiation (primarily, but not only, the angular distribution) detected by the PPG sensor depends on the spatial distribution of the dye. But since the PPG sensor does not have angular selectivity, it is not possible to explicitly separate the influence of the molecular and cellular structure of the blood on the PPG sensor signal. At the same time, the signal from the BIA sensor is predominantly sensitive to the cellular structure of the blood with an almost complete lack of sensitivity to its molecular structure. Thus, taking into account the characteristics of the cellular structure of the blood (heterogeneity of the spatial distribution of the dye) through the signal of the BIA sensor allows one to correctly determine the concentration of the dye (the molecular structure of the blood). In other words, we can formulate the following statement that the set of characteristics extracted from the optical signal of the PPG sensor describes both the molecular and cellular structure of blood, and the set of characteristics extracted from the electrical signal of the BIA sensor describes the cellular structure of blood.

[0063] The proposed wearable device, method and system provide the possibility of personal, non-professional use and the ability to instantaneously or continuously determine blood parameters, for example, hemoglobin concentration (cHb), total hemoglobin mass (tHb), hematocrit (HBT), total blood volume (BV ), plasma volume (PV), mean erythrocyte hemoglobin concentration (MCHC), etc. with high accuracy even when estimating the marginal values of the parameters being determined.

Brief description of drawings

[0064] The above and other features and advantages of the present invention will be explained in the following description with reference to the accompanying drawings. The following specific exemplary embodiments of the claimed invention, taken in conjunction with the drawings, are not intended to limit the scope of the invention. Upon examination of the present description, additional embodiments, modifications or equivalents of the present invention will be apparent to those skilled in the art, and all such embodiments, modifications and equivalents are deemed to be included in the present invention.

[0065] The drawings are provided solely to assist in understanding the specification and should not be construed in any way as limiting the scope of the invention. The drawings show the following:

Fig. 1 schematically illustrates the dependence of the PPG signal waveform on wavelength and user.

Fig. 2A schematically illustrates the absorption spectra of whole blood at different hemoglobin concentrations; fig. 2B schematically illustrates the absorption spectra of biological tissue (blood + other components) at the same hemoglobin concentration.

Fig. 3A schematically illustrates a first conventional approach to processing a PPG signal; fig. 3B schematically illustrates a second conventional approach to processing a PPG signal.

FIG. 4A illustrates the effect of using the first conventional PPG signal processing approach to determine the hemoglobin concentration of FIG. 3A; fig. 4B illustrates the effect of using the second conventional PPG signal processing approach as applied to the determination of hemoglobin concentration in FIG. 3B. In fig. 4B, the term “critical point” means a set of critical points for the waveform of a signal at each of the specified wavelengths.

Fig. 5 schematically illustrates the step of converting a PPG signal from time to frequency domain.

Fig. 6 illustrates the key stages of PPG signal conversion.

Fig. 7 illustrates a key feature of the conversion step of FIG. 5.

Fig. 8 illustrates the feature of converting a PPG signal into the frequency domain.

Fig. 9 illustrates the features of signal analysis in the frequency domain.

Fig. 10A, B illustrate a block diagram and a schematic diagram of a wearable device with a blood parameter detection function. Fig. 10B illustrates the location of the BIA sensor electrodes in a Samsung watch.

Fig. 11 illustrates an exemplary circuit for measuring BIA signals in the wearable device of FIG. 10.

Fig. 12 illustrates a graph of the deviation of hemoglobin values measured by non-invasive devices of the prior art for a plurality of volunteers from hemoglobin levels measured by laboratory analysis as a function of the average of these pairwise measurements.

Fig. 13 illustrates graphs of hemoglobin values measured by non-invasive devices of the prior art for a variety of volunteers with low hemoglobin (upper graph) and with normal hemoglobin (lower graph).

Fig. 14 illustrates the key features (KO) of determining blood parameters according to the present disclosure.

Fig. 15 illustrates KO1 according to the present disclosure in more detail.

Fig. 16 illustrates CO2 according to the present disclosure in more detail.

Fig. 17 illustrates in more detail the construction of a predictive model for K02 according to the present disclosure.

Fig. 18 illustrates the results of determining a hemoglobin level using a predictive model according to the present disclosure.

Fig. 19 illustrates Embodiment 1 according to the present disclosure.

Fig. 20 illustrates embodiment 2 according to the present disclosure.

Fig. 21 illustrates a potential embodiment 3 according to the present disclosure.

Fig. 22 illustrates the results of determining hemoglobin levels throughout the day for a volunteer with low hemoglobin using a prior art device (upper graph) and using a wearable device using the predictive model of the present disclosure (lower graph).

Fig. 23 illustrates the relative change in blood volume, plasma volume and red blood cell volume in an athlete depending on the number of days of training.

Fig. 24 schematically illustrates the features of using PPG and B1A sensors in a wearable device.

Detailed Description of Embodiments of the Invention

[0066] The present invention is a wearable device with the function of determining at least one parameter of the user's blood, containing at least one photoplethysmographic (PPG) sensor configured to irradiate the user's tissue with radiation of at least two different wavelengths and record at least two optical PPG signals at said at least two different wavelengths in a reflective mode, a bioimpedance (BIA) sensor comprising two pairs of electrodes and configured to pass alternating current through the user's body and detect corresponding electrical signals, wherein the wearable device is configured with the ability to extract characteristics of the molecular and cellular structure of blood from the signals registered by the mentioned sensors and form a set of characteristics of the structure of blood that determine the state of the structure of the blood, determine the value of at least one parameter of the user’s blood based on the obtained set of characteristics of the molecular and cellular structure of blood and information from pre-compiled Database.

[0067] According to an embodiment, the wearable device further comprises a housing, a processor, a battery and a storage device housed in the housing.

[0068] According to an embodiment, the at least one blood parameter is hemoglobin concentration (cHb), total hemoglobin mass (tHb), hematocrit (HCT), total red blood cell volume (tEV), plasma volume (PV).

[0069] According to an embodiment, the determination of blood parameter values is based on comparing the generated set of blood structure characteristics with sets of blood structure characteristics from the database using a trained predictive model; wherein the database is a database in which sets of blood structure characteristics are compared with specific reference values of at least one blood parameter for a plurality of users, determined by a laboratory method.

[0070] In an embodiment, the predictive model is trained on a compiled database using a machine learning algorithm or a neural network.

[0071] According to an embodiment, the compiled database contains profile data of a plurality of users.

[0072] According to an embodiment, the wearable device is configured to, when processing PPG signals and extracting characteristics of the molecular structure of blood, convert the at least two PPG signals from the time domain to the frequency domain or time-frequency domain, select from all PPGs signals the most significant signal and use it as a reference signal, extract harmonics from the reference signal and use them as coordinates, decompose all PPG signals along the mentioned coordinates to obtain a set of amplitude-phase characteristics of harmonics, describe the converted PPG signals relative to harmonics with obtaining a set of characteristics of the molecular structure of blood.

[0073] According to an embodiment, the wearable device is configured to, when processing PPG signals and extracting characteristics of the molecular structure of blood, determine the value of the fundamental (first) harmonic (f ₀ ), the value of the amplitude-phase characteristics (t _λ ⁰ ) of harmonics at least at least two PPG signals with different wavelengths.

[0074] According to an embodiment, the wearable device is configured, when processing BIA signals and extracting blood cellular characteristics, to detect a voltage drop across the electrodes to obtain at least one body impedance parameter of the user, such as a body impedance modulus (Z) value user, the phase angle (ϕ) of the user's body impedance at different frequencies, and calculate the total body fluid (TBW), the ratio of the amount of intracellular fluid (ICW) to the amount of extracellular fluid (ECW).

[0075] According to an embodiment, said different frequencies are in the ranges of 20-40 kHz and 200-300 kHz, respectively.

[0076] According to an embodiment, said at least one PPG sensor comprises at least one radiation source comprising two or more veto-emitting diodes, and at least one radiation receiver, preferably a broadband photodetector.

[0077] According to an embodiment, said different wavelengths may be in the ranges of visible radiation (approximately 400-800 nm), near-infrared radiation (approximately 800-1700 nm) and short-wave infrared radiation (approximately 1700-2500 nm). In an embodiment, one of said different wavelengths is preferably in the range of 495-570 nm, and more preferably is 530 nm.

[0078] According to an embodiment, the transformation of PPG signals from the time domain to the frequency domain comprises a Fourier transform or a Hilbert-Huang transform, and the transformation to the time-frequency domain comprises a wavelet transform, the most significant signal having the best signal-to-noise ratio, the highest amplitude and/or least noise, extracting harmonics from a reference signal involves determining the coordinates of the fundamental harmonic and calculating the coordinates of other harmonics, describing PPG signals relative to harmonics includes measuring for each harmonic the amplitude, curve area, peak width at half height, and/or signal-to-noise ratio with obtaining a set of amplitude-phase characteristics of harmonics.

[0079] According to an embodiment, the wearable device further includes a storage device configured to store a database.

[0080] According to an embodiment, the wearable device is further configured to correlate a determined blood parameter value using a laboratory-derived blood parameter value, update the database, and refine the predictive model using a machine learning algorithm or neural network.

[0081] According to an embodiment, the wearable device further comprises an input and output device configured to input user profile data and display a determined blood parameter value, and a communication module configured to exchange information with a remote server and/or cloud storage.

[0082] According to an embodiment, the wearable device is configured to be placed on the wrist.

[0083] According to an embodiment, the wearable device is a smart device, preferably a smart watch or a fitness tracker.

[0084] The present invention also relates to a method for determining at least one blood parameter of a user, comprising the steps of irradiating the user's tissue with radiation of at least two different wavelengths, detecting photoplethysmographic (PPG) signals in the time domain at the at least two different wavelengths in reflection mode, convert the detected PPG signals from the time domain to the frequency domain, describe the converted PPG signals with respect to harmonics to extract a set of characteristics of the molecular structure of blood, pass alternating current through the user’s body, record the corresponding bioimpedance (BIA) electrical signals, caused by the passage of alternating current, describe the registered BIA signals with extracting a set of characteristics of the cellular structure of blood, form a set of characteristics of the molecular and cellular structure of blood that determine the state of the blood structure, determine the value of at least one parameter of the user’s blood based on the obtained set of molecular and cellular characteristics blood structure and information from a pre-compiled database.

[0085] According to an embodiment of the method, at the stage of describing PPG signals and extracting characteristics of the molecular structure of blood, the most significant signal is selected from all PPG signals and used as a reference signal, harmonics are extracted from the reference signal and used as coordinates, and laid out all PPG signals at the mentioned coordinates and determine the value of the fundamental harmonic (f ₀ ), the magnitude of the amplitude-phase characteristics of the harmonics of at least two PPG signals with different wavelengths, at the stage of describing the BIA signals and extracting the characteristics of the cellular structure of the blood are obtained at least one parameter of the user's body impedance, such as the magnitude (Z) of the user's body impedance modulus, the phase angle (ϕ) of the user's body impedance at different frequencies, and calculate the total body fluid (TBW), the ratio of the amount of intracellular fluid (ICW) to the amount of extracellular fluid liquids (ECW).

[0086] According to an embodiment of the method, at the stage of determining the value of blood parameters, the generated set of blood structure characteristics is compared with sets of blood structure characteristics from a pre-compiled database, each of which corresponds to a specific reference value of at least one blood parameter for a plurality of users, determined by the laboratory method using a trained predictive model.

[0087] According to an embodiment of the method, irradiation is carried out using at least one radiation source, which is two or more light-emitting diodes, and detection of PPG signals is carried out using at least one radiation receiver, preferably a broadband photodetector.

[0088] According to an embodiment of the method, the conversion of PPG signals from the time domain to the frequency domain is carried out by the Fourier transform or the Hilbert-Huang transform, and the conversion to the time-frequency domain is carried out by the wavelet transform, the most significant signal having the best signal-to-noise ratio , highest amplitude and/or least affected by noise, extracting harmonics from a reference signal involves determining the coordinate of the fundamental harmonic and calculating the coordinates of other harmonics, describing PPG signals relative to harmonics includes measuring for each harmonic amplitude, curve area, peak width at half height, and/or or signal-to-noise ratio to obtain a set of amplitude-phase characteristics of harmonics.

[0089] According to an embodiment, the method further includes correlating the determined value of the at least one blood parameter using a laboratory-derived blood parameter value, updating the database, and refining the predictive model using a machine learning algorithm or neural network.

[0090] The present invention further relates to a system for determining the value of at least one parameter of a user's blood, comprising the aforementioned wearable device and a remote server and/or cloud storage, wherein the system contains means for exchanging information between the wearable device and the remote server and/or cloud storage storage.

[0091] According to an embodiment of the system, the database is designed to be stored on a storage device, a remote server and/or cloud storage and is configured to access user data from external devices through the remote server and/or cloud storage.

[0092] According to an embodiment of the system, user profile data and the results of previous determinations of the value of the at least one blood parameter are stored on the storage device, remote server and/or cloud storage.

[0093] According to an embodiment, the system further includes means for accessing user profile data and the results of previous determinations of the value of the at least one blood parameter from other electronic devices.

[0094] The proposed wearable device, method and system provide the ability to non-invasively, accurately, instantaneously or continuously monitor various blood parameters, such as, for example, hemoglobin concentration (cHb), total hemoglobin mass (tHb), hematocrit (HCT), total red blood cell volume ( tEV), blood volume (BV), plasma volume (PV) even at the extreme values of the controlled parameters and are suitable for non-professional use.

[0095] Features of the present disclosure and exemplary embodiments of the invention will now be described with reference to the drawings. Those skilled in the art will appreciate that the various exemplary embodiments should not be construed as limiting the scope of the claimed invention, and that other material and technical means equivalent or similar to those listed below may be employed by those skilled in the art to perform the various operations, functions, method steps and etc. described below. This detailed description is not intended to limit the scope of the claimed invention, which is defined only by the appended claims.

[0096] It should be noted that in the following, examples of hemoglobin level measurement are mainly given to explain the principles of the present invention. However, the present invention is not limited to this and can be used to determine many blood parameters (hemoglobin, hematocrit, total red blood cell volume, blood plasma volume, red blood cell count, etc.) of the user by predicting the molecular and cellular structure of the blood based on the entered characteristics (see . table 1), which include at least one parameter of the user's body impedance, such as the magnitude (Z) of the user's body impedance modulus, the phase angle (ϕ) of the user's body impedance at certain frequencies (at least at two frequencies, such as 20-40 kHz and 200-300 kHz), the total amount of fluid in the body (TBW), the ratio of the amount of intracellular fluid (ICW) to the amount of extracellular fluid (ECW), the value of the first harmonic (f ₀ ), the value of amplitude-phase characteristics (t _λ ⁰ ) harmonics (at least the amplitude and total energy of at least two harmonics within the spectral band specified relative to the central frequency, the position of which is determined by the reference frequency corresponding to the heart rate) of at least two PPG signals with different wavelengths (I).

[0097] Amplitude-phase characteristics represent the original characteristics extracted from the PPG signal at each wavelength used for measurement, namely:

apparent power (e.g. area under the curve) of harmonics at frequencies respectively, where f ₀ is the reference frequency corresponding to the heart rate; amplitudes of harmonics at corresponding frequencies

p ₀ - power of the constant component.

In addition, to determine blood parameters, various derived characteristics (signs) are used:

[0098] Knowledge of the above parameters and characteristics determined by PPG and BIA signals makes it possible to accurately determine a number of important blood parameters, for example, hemoglobin concentration (cHb), total hemoglobin mass (tHb), hematocrit (HCT), total red blood cell volume (tEV), blood volume (BV), plasma volume (PV), etc., including the boundary values of these parameters.

[0099] Table 1 below shows an example of a set of input data used to determine blood parameters and the source of their determination.

[0100] Features of measuring PPG and B1A signals will now be discussed with reference to the drawings.

[0101] In FIG. Figure 1 schematically illustrates the dependence of the PPG signal shape on the wavelength and user. It can be clearly seen that the waveform of the pulse signal depends not only on the wavelength of the primary radiation, but also on the specific user. The main factors influencing the shape of the PPG signal are absorption and scattering of radiation in tissue, including blood, hydrodynamics of blood vessels, and mechanical deformation in tissue. In this case, for example, the concentration of hemoglobin in the patient’s blood is determined by the absorption of radiation in the tissue, or more precisely, as will be discussed below, by the pulsating (AC) component of absorption in arterial blood, and the contribution made to the signal due to the scattering of radiation in the tissue is hydrodynamics blood vessels and mechanical deformation is a source of error and must be eliminated. Taking into account the characteristics of the propagation of optical radiation in biological tissue, which will be discussed below, the above processes as sources of error lead to a significant change in the pulse waveform depending on the wavelength, and the waveform is specific to the optical properties of the tissue of each individual user. Thus, to correctly measure the hemoglobin concentration in the blood (determined by the absorption of radiation in arterial blood), it is necessary to analyze the shape of the PPG signal. The important points here are the following: the need for spectroscopic measurements at multiple wavelengths, the need to take into account that at some wavelengths the PPG signal can be very weak, the signal can have a shape that is difficult to process, the need to measure a weak PPG signal that has a complex shape.

[0102] It should also be noted that there is a difference in the mechanisms of scattering and absorption of radiation in the studied volume of biological tissue in transmission mode and in reflection mode. In the transmission mode used in medical and laboratory applications, radiation from the light source to the light detector at two different wavelengths λ ₁ and λ ₂ travels approximately the same optical path through the patient's tissue and interacts with the same areas of tissue, so the PPG signal in this mode has a similar shape at different wavelengths, for example, 530 nm and 655 nm. In the reflection mode used in wearable devices, such as smart watches and fitness bracelets, the radiation from the light source to the light detector at two different wavelengths λ ₁ and λ ₂ travels a different path and interacts with different areas of the patient's tissue. For example, radiation with wavelength λ ₁ interacts only with the patient's skin and partly with muscle tissue, including capillary blood vessels, while radiation with wavelength λ ₂ interacts with the patient's skin, with muscle tissue, including not only capillary blood vessels, but also venous and arterial vessels, so the PPG signal in reflection mode has very different shapes at different wavelengths. However, to correctly measure blood parameters, the same volume of tissue being examined is required, which is difficult to achieve in reflection mode.

[0103] Taking into account the above, the main problems of spectral analysis of a PPG signal are the following:

- The AC component of the pulse signal has a complex shape, that is, it is not a harmonic or sinusoidal signal;

- the shape of the AC component depends on the wavelength of the radiation, that is, the shape of the pulse signal carries information, including about the spectral properties of biological tissue;

- limited operation in reflection mode.

[0104] When searching for a solution, it is important to consider the significantly different optical paths for different wavelengths, which is less important in the transmission mode and critical for the reflection mode (for example, for smart watches), the need to measure the same tissue area, the different pulse shape due to difference in scattering and absorption of radiation. Thus, for reflection mode, it is preferable to analyze the PPG waveform.

[0105] In FIG. Figure 2A schematically illustrates the absorption spectra of whole blood at different hemoglobin concentrations obtained using photoplethysmography (PPG) at several wavelengths. Dashed lines show continuous absorption spectra of whole blood at two different hemoglobin concentrations, and circles show absorption measured at several discrete points. From fig. 2A shows that under ideal conditions, when there is whole blood in the cuvette, the hemoglobin concentration can be determined by measuring the transmittance of optical radiation at any one wavelength. However, the whole blood case is ideal, and in real cases it is not possible to perform measurements on whole blood in a non-invasive manner.

[0106] In FIG. Figure 2B schematically illustrates the absorption spectra at the same hemoglobin concentration, but in the presence in one case of other blood components or other tissues, including melanin, lipids, proteins, etc. From fig. 2B shows that at some wavelengths, for example, at λ _m , noticeable absorption of optical radiation occurs due to the presence of other components of blood or other tissues. Thus, other blood components or other tissues may absorb at the same wavelengths as hemoglobin. Therefore, it is impossible to say definitively whether the absorption at wavelength λ _m has changed due to hemoglobin or something else.

[0107] As can be seen from FIG. 2A and 2B, the ideal case in blood testing is to measure blood absorbance only, where absorbance at any wavelength corresponds to changes in blood hemoglobin concentration. In actual practice, the absorption of entire biological tissue, including blood, is measured, and other components may also absorb radiation at the same wavelengths, affecting the pulse signal. Therefore, measuring absorption at a single wavelength may lead to incorrect results. Therefore, in this case, a spectroscopic approach to measurements is necessary, i.e. To correctly determine the hemoglobin concentration, it is necessary to measure the absorption spectrum at several wavelengths.

[0108] In FIG. 3A schematically illustrates the first traditional approach to processing a PPG signal, which consists of measuring the area under the curve (AUC) of the AC component of the PPG signal, i.e. determining the integral area of the PPG signal shape. It is believed that the intensity of the AC component measured in this way is proportional to the optical transmittance of the measured volume of tissue and can be converted into absorption in this area according to the Bouguer-Lambert-Beer law. This is the simplest measurement approach, usually used in transmission mode. Shown in FIGS. 3A signals have approximately the same integral area, but different curve shapes. Since this approach does not take into account information about the pulse shape, the measurement of optical transmission and absorption in the reflection mode is carried out with a large error.

[0109] In FIG. 3B schematically illustrates the second traditional approach to PPG signal processing, which consists of estimating the shape of the PPG signal curve. The first peak of the pulse wave, corresponding to the anacrotic period of the pulse wave, is formed during systole (the “systolic peak” is designated SIS). The amplitude of the anacrotic phase is also called the amplitude of the pulse wave and corresponds to the stroke volume of blood during cardiac output, thus providing indirect information about the degree of inotropic effect. The second peak of the pulse wave, corresponding to the dicrotic period of the pulse wave, is formed due to the reflection of blood volume from the aorta and large great vessels and partially corresponds to the diastolic period of the cardiac cycle (“diastolic peak” is designated as DIA). The dicrotic phase provides information about vascular tone. The top of the pulse wave corresponds to the largest volume of blood, and its opposite part corresponds to the smallest volume of blood in the tissue area under study. On the descending front of each wave there is a noticeable depression - the dicrotic incisura, which corresponds to the closure of the aortic valve (the "dicrotic incision" is designated DN). The nature of the pulse wave depends on the elasticity of the vascular wall, pulse rate, volume of the tissue area being examined, width of the lumen of the vessels, etc. It is believed that the frequency and duration of the pulse wave depends on the characteristics of the heart, and the size and shape of its peaks depend on the condition of the vascular wall. If the pulse wave has clearly defined systolic and diastolic peaks, as well as a valley between them (the upper PPG signal in Fig. 3B, a rare case of an ideal curve, characteristic of young, healthy people with a strong cardiovascular system), an approach to determining the optical properties tissue based on the PPG signal curve shape works well. In most cases, these features of the pulse wave are not clearly expressed (the middle graph in Fig. 3B, typical for relatively healthy adults); this approach works in many cases, but is prone to errors, since it requires individual adjustment of the algorithm for each curve, which means it is difficult to optimize processing. However, in some patients, the systolic and diastolic peaks and the trough between them may be difficult to detect or even indistinguishable (bottom graph in Fig. 3B, a rare case of a poor curve, characteristic of older people with weak cardiovascular systems or young unhealthy people ). In this case, the shape of the wave signal curve is characterized by the same parameters that have a clear physiological meaning, as described above, but the formalization of the algorithm for finding these points is difficult and therefore the shape is characterized inaccurately.

[0110] Additionally, in FIG. 4A illustrates the effect of using the first conventional approach (AUC determination) to process the PPG signal of FIG. 3A using the example of determining hemoglobin concentration. In fig. 4A the dashed curves show the absorption spectra of whole blood with different concentrations X and Y of hemoglobin, that is, the dependence of absorption on the radiation wavelength, the circles indicate absorption values determined from the areas of the PPG signal, that is, from the integral areas of the AC components of the signal. At the same time, the circles for different concentrations on the presented graphs are located close to each other and are noticeably shifted from the true absorption values. This means that determining the hemoglobin concentration from the areas of the AC component of the PPG signal results in an incorrect concentration value. Moreover, with this approach it is often impossible to distinguish between two different blood hemoglobin concentrations.

[0111] Regarding the use of the second traditional approach to processing the PPG signal in relation to determining the hemoglobin concentration using the critical points of the pulse signal, as shown in FIG. 4B, in one case, at the hemoglobin concentration Y (upper curve), the approach allows for fairly accurate measurements, however, in other cases (lower curve), it is generally impossible to determine the absorption of radiation at some wavelengths due to the inability to distinguish critical points on the PPG signal curve, and This means that it is impossible to determine the hemoglobin concentration from the shape of the pulse signal curve.

[0112] Unlike the approaches discussed, the method of the present invention can be applied to any pulse waveform, as will be described below.

[0113] To obtain and subsequently process the PPG signal, the user's tissue is first irradiated at at least two wavelengths using a light source, such as an LED or laser diode. One of the at least two different wavelengths is preferably in the range 495-570 nm, that is, the LED emits green light, since it is usually green light that provides the most informative signal when determining, for example, hemoglobin concentration. However, the present invention is not limited to this range of radiation and the various wavelengths mentioned may be in any of the ranges of visible radiation (approximately 400-800 nm), near-infrared radiation (approximately 800-1700 nm) and short-wave infrared radiation (approximately 1700-2500 nm). nm). A reflectometric method is used, that is, the user's tissue is irradiated and then PPG signals from the user's tissue are detected at the at least two different wavelengths in the reflection mode. In the example illustrated in FIG. 5, signals are detected at six wavelengths. However, the number of PPG signals at different wavelengths is not particularly limited. In this case, the detected signals are presented in the time domain (time series). In general, several representations of a signal are possible: in the time domain, when f(t) is expressed as a function of time, in the frequency domain, when the spectrum (i.e., the amplitudes of the various harmonics) is defined, or in the time-frequency domain. Signals can be converted from time to frequency/time-frequency domain using various types of transformations. According to an embodiment of the invention, the tool for representing the detected signal in exponential terms is the Fourier transform. The function F(ω) is the direct Fourier transform of the signal f(t). F(ω) represents the signal f(t) in the frequency domain. The temporal representation defines a certain signal at each moment in time, while the frequency representation characterizes the relative amplitudes of the frequency components of the signal, i.e. harmonics

[0114] By performing a Fourier transform based on the time domain characteristics of a signal, its harmonic amplitude-phase characteristics (instantaneous spectrum, spectral density, etc.) can be obtained. For example, the Fourier transform allows each PPG pulse to be converted into a set of amplitudes and phases, so the resulting set of features contains all the information necessary for the machine learning model used to determine the hemoglobin concentration. In this case, for calculations, it is convenient to describe a set of signal characteristics using complex numbers. Any of these representations completely defines the signal. However, as explained in detail in the "Background of the Invention" section, processing the PPG signal in the frequency domain avoids several disadvantages of the prior art.

[0115] However, the invention is not limited to the use of the Fourier transform and other transforms can be used. For example, transforming signals from the time domain to the frequency domain can also be done using the Hilbert-Huang transform, and to the time-frequency domain using the wavelet transform or bilinear time-frequency distribution.

[0116] The Hilbert-Huang transform (HHT) is a method of empirical mode decomposition (EMD) of nonlinear and non-stationary processes, i.e. decomposition into amplitude-time components, and subsequent Hilbert spectral analysis (HSA). NNT is a time-frequency analysis of signals and does not require an a priori functional basis for the transformation. The basis functions are obtained adaptively directly from the data by screening procedures for “empirical mode” functions. Instantaneous frequencies are calculated from the derivatives of phase functions by the Hilbert transform of basis functions. The main informative features of the Hilbert-Huang transform are the amplitude, instantaneous frequency and phase of the empirical modes.

[0117] The Wavelet Transform (WT) is an integral transform that is the convolution of a wavelet function with a signal. The wavelet transform converts a signal from a time representation to a time-frequency representation and is a generalization of spectral analysis, a typical representative of which is the classical Fourier transform. The term “wavelet” translated from English means “small (short) wave.” Wavelets are a generalized name for families of mathematical functions of a certain form, which are local in time and frequency, and in which all functions are obtained from one base (generating) through its shifts and stretches along the time axis. Wavelet transform algorithms make it possible to take into account local changes in PPG signals and carry information in three-dimensional format - amplitude, frequency, time. Typically, wavelet transforms are divided into discrete (DWT) and continuous (CWT). DWT is used for signal conversion and coding, CWT is used for signal analysis. Wavelet transforms are now increasingly being used for a huge variety of applications, often replacing the conventional Fourier transform.

[0118] Bilinear time-frequency distributions or quadratic time-frequency distributions arise in a subfield of signal analysis and processing called time-frequency signal processing, as well as in the statistical analysis of time series data. Such methods are used in cases where the frequency composition of the signal can change over time. Such analysis was formerly called time-frequency signal analysis and is now more commonly called time-frequency signal processing due to advances in the use of these techniques to solve a wide range of signal processing problems. Compared to other time-frequency analysis methods, such as the short-time Fourier transform, the bilinear transform may not have greater clarity for most practical signals, but it provides an alternative basis for exploring new definitions and new methods. All bilinear distributions are mutually convertible to each other.

[0119] At the bottom of FIG. Figure 5 illustrates PPG signals at two different wavelengths in the time and frequency domains.

[0120] The present invention allows the precise pulse waveform to be characterized from PPG signals, including weak and/or complex signals. The key processing steps for PPG signals, illustrated in FIG. 6, are:

- conversion of detected PPG signals from the time domain (time series) to the frequency (or time-frequency) domain;

- selecting from all detected PPG signals at different wavelengths in the frequency domain the most significant signal, which is characterized by the best signal-to-noise ratio, the highest amplitude and which is least affected by noise, and using the selected signal as a reference signal;

- extracting the fundamental harmonic (fundamental component) from the reference signal and, on its basis, other harmonics of the reference signal and using them as coordinates along which the rest of the PPG signals are laid out, that is, the amplitude-phase characteristics of the harmonics are obtained.

[0121] In FIG. 7 illustrates a key feature of the PPG signal conversion step of FIG. 5, which is that when converting a PPG signal from the time to frequency domain, information about the periodicity of the time signal is transferred to the position of the first harmonic of the frequency domain, and information about the shape of the signal (peaks, depressions, notches) is transferred to harmonics of higher orders. Ultimately, data sets are obtained for each wavelength of the pulse signal in the frequency domain, each of which is characterized by specific characteristics (for example, amplitudes a ₁₀ , a ₁₁ ...a _1n at harmonic frequencies f ₀ , f ₁ , ... f _n ), as shown at the bottom of Fig. 6. In this case, the position, number, amplitude, width of the resulting harmonics, which are easily measured characteristics, completely describe the pulse wave in the frequency domain. Thus, converting PPG signals into the frequency domain makes it possible to extract the amplitude-phase characteristics of the signal harmonics.

[0122] Taking into account the above, processing PPG signals in the frequency domain results in the following advantages over processing in the time domain:

- a finite number of characteristics instead of continuous time series data;

- unambiguous connection between the characteristics and the shape of the PPG signal;

- the presence of a new coordinate system (harmonic frequencies), determined from one of the signals, which allows all signals to be decomposed uniformly according to a single coordinate system.

[0123] If you try to analyze the pulse waveform by critical points, then each signal will have to be processed independently. In this case, the position of the critical points may “float”, thereby disrupting the unambiguity in the description of the signal shape at different wavelengths. In addition, in the case of weak signals and signals with a high level of noise, finding critical points may in principle be impossible. Thanks to the above-mentioned coordinate system (frequencies and their positions), it is possible to find the characteristics (for example, amplitudes) of these frequencies even in the spectrum of a signal with a high noise level.

[0124] In FIG. 8 illustrates the feature of converting a PPG signal into the frequency domain. Shown here is a high quality PPG reference signal at wavelength λ ₂ . and the usual low-quality PPG signal at other wavelengths λ _1, λ ₃ , etc. After converting the high quality signal into the frequency domain, the position and characteristics of the fundamental harmonic f ₀ can be easily measured. With a similar conversion of a low-quality signal, it is impossible to reliably determine the position of the fundamental harmonic f ₀ in the frequency domain. Moreover, the intense false peaks, shown in the graph on the right with broken circles, hide the necessary characteristics. However, the advantage of the present invention is the stability, or more precisely, the independence of the PPG reference signal from interference and low quality signals. After performing the Fourier transform, the fundamental harmonic f ₀ of the signal in the frequency domain is the first harmonic corresponding to the periodicity of the PPG signal, i.e. Heart rate. In the case of other time-frequency transformations, the main component is, for example, the frequency component of the greatest power and the most stable over time, which is not directly related to physiological parameters. Moreover, the position of the fundamental harmonic f ₀ of a high-quality signal in the frequency domain corresponds to the position of the fundamental harmonic f ₀ of low-quality signals.

[0125] Additionally, another feature of the PPG signal in the time domain should be noted, which affects the need to select an appropriate wavelength for measurements. According to the prior art, the best PPG signal is obtained using green light (-530 nm). However, this is not always the case. The pulse signal greatly depends on the color of a person’s skin, his nationality, tan, skin pigmentation, etc. The selection of a higher quality PPG signal is usually based on determining the highest signal-to-noise ratio. For example, for a fair-skinned person, the best PPG signal is actually obtained in green light, and is used as a reference signal. However, dark-skinned people have their own characteristics, the main of which is related to the fact that green light penetrates to a shallow depth into the patient’s biological tissue. Because green light in dark skin is almost completely absorbed by melanin, it does not reach the blood vessels. At the same time, red/IR light penetrates deeper into tissue because melanin absorbs red/IR light much less than green light and does not affect the PPG signal. Therefore, for dark-skinned people, the best result will be to use the PPG signal obtained with red/IR light as a reference signal, rather than using green light as for light-skinned people.

[0126] Thus, individual settings for operation with the PPG sensor are necessary, and the appropriate reference wavelength should preferably be determined individually for each user of the wearable device.

[0127] In FIG. Figure 9 further illustrates the features of signal analysis in the frequency domain. A signal with wavelength λ ₂ was selected as the reference PPG signal. In the top graph of Fig. Figure 9 shows the characteristics of the first f ₀ and second f ₁ harmonics of the PPG signal at wavelength λ ₂ , namely the amplitudes a ₂₀ and a ₂₁ of these harmonics. Let us note once again that in the frequency domain the positions of the harmonics are identical for signals of all wavelengths of the visible radiation spectrum. In other words, the harmonic positions of the analyzed PPG signal with wavelength λ ₁ shown in the lower graph coincide with the harmonic positions of the reference PPG signal with wavelength λ ₂ . Consequently, PPG signals at all wavelengths can be analyzed equally and simultaneously as soon as the harmonics are determined from the reference PPG signal. In this case, the positions of higher order harmonics are uniquely related to the position of the fundamental harmonic by the following relationships: f ₁ =2f ₀ ; f ₂ =3f ₀ , etc. Thus, knowing the positions of the harmonics of the reference signal, you can easily determine the positions, and therefore the amplitudes, of the corresponding harmonics of the analyzed PPG signal with wavelength λ ₁ , for example, the amplitudes a ₁₀ and a ₁₁ of the first and second harmonics.

[0128] Using the method described above, based on the position of the fundamental harmonic f ₀ of the reference PPG signal, the characteristics, in particular, the amplitudes of all PPG signals at other wavelengths, are determined. The advantage of this technique is the simple parameterization of the PPG signal waveform simultaneously at all radiation wavelengths.

[0129] It should also be noted that frequency domain signal analysis makes the blood molecular characterization algorithm somewhat robust to motion artifacts, provided that the frequency and duration of these artifacts are not the same as the underlying signal. The frequency of artifacts may coincide with the frequency of the PPG signal, but motion artifacts are non-stationary, i.e. their characteristics change over time, so by accumulating the signal it is possible to enhance the essentially stationary components of the PPG signal and suppress the contribution of motion artifacts to the overall signal spectrum.

[0130] Next, with reference to FIG. 10A, 10B will show a schematic diagram of a wearable device 201 with a function for detecting the user's blood parameters.

[0131] With reference to FIG. 10A, a block diagram of a preferred embodiment of a wearable device 201 will be described, comprising a housing 202, a processor (e.g., microprocessor) 203, a battery 204, at least one PPG sensor 208, an input device 209, an output device 210, a display 211, a storage device. 212, a communication module 213 including a wireless communication module 223 and a wired communication module 224, and a bioimpedance measurement unit 218. The input device 209 and the output device 210 may be configured as a single input and output device. The wearable device 201 may include additional sensors, such as inertial motion sensors, gyroscopes, accelerometers, an electrocardiogram (ECG) measurement sensor, an atmospheric pressure sensor, a humidity sensor, a Hall sensor, an ambient light sensor, etc. In embodiments, the wearable device 201 may be a smart device, in particular, a smart watch or fitness bracelet, a medical wearable device for monitoring health status with the function of determining hemoglobin concentration, etc. The wearable device is an electronic wearable device and can be configured with the ability to establish a wired or wireless communication channel with external devices, for example, devices 225, 226, such as smartphones, wearable fitness bracelets, voice assistants, smart TVs, smart watches, etc., or a server 227 and with the ability to transmit data via network 228 or cloud storage 229.

[0132] The wearable device 201 with the PPG sensor 208 in reflection mode is capable of detecting light backscattered and/or backreflected from biological tissues of the user, including skin, bones, blood, blood vessels.

[0133] Wearable devices with a PPG sensor use a reflection mode to conveniently place the device, for example, on the user's hand. When worn on the user's arm or other part of the body, the PPG sensor is in contact with the skin. However, a PPG sensor measuring in reflective mode can be affected by motion artifacts and pressure fluctuations. Any movement, such as physical activity, can result in motion artifacts that distort the PPG signal and limit the accuracy of physiological parameter measurements. Pressure fluctuations applied to the sensor, such as the contact force between the PPG sensor and the measurement site, that is, the force with which the device is pressed against the user's skin, can deform the geometry of the artery due to compression. Thus, in reflection mode, the amplitude of the variable component of the PPG signal can be influenced by the pressure applied to the skin. Such influences on PPG signal measurements are described in more detail in Toshiyo Tamura, Current Progress of Photoplethysmography and Sp02 for Health Monitoring, Biomedical Engineering Letters (2019) 9, cc. 21-36 (https://doi.org/10.1007/sl3534-019-00097-w).

[0134] The PPG sensor circuitry may include an amplifier, a high-pass filter (about 0.1 Hz) to cut off the DC component and produce pulsating changes in the signal, a low-pass filter (about 30 Hz) to eliminate high-frequency noise, and a microprocessor. The frequency range used depends on the circuit design. The PPG sensor may have a wireless module for transmitting data to an external device.

[0135] The bioimpedance measurement unit (BIA sensor) 218 may include a biocontact circuit (contact circuit with the user's body) containing 4 electrodes, an impedance measurement module containing a current-voltage measurement module, a current-voltage path (circuit) configuration module , an impedance calculation module, at least one leakage current compensation module, and optionally a microprocessor.

[0136] In the preferred embodiment of the present invention illustrated in FIG. 10B, the wearable device 201 is in the form of a smart watch that is designed to be placed on the user's wrist. In other embodiments of the present invention, the wearable device 201 may be configured to be placed on other parts of the user's body, such as a finger.

[0137] In FIG. 10B illustrates the wearable device 201 in a top view and a bottom view. According to this embodiment, the body 202 of the wearable device 201 includes a first side 214 (or front surface), a second side (or rear surface) 215, and a side surface 216 surrounding the space between the first side 214 and the second side 215, and fastening members 217 for for detachably attaching the wearable device 201 to the wrist, for example, using a watch strap. In addition, the wearable device illustrated in FIG. 10B has four BIA sensor electrodes, two of which 251, 253 are integrated into the control buttons of the device, and the other two 252, 254 are located on the back side 215 of the wearable device. In fig. 10B illustrates an actual Samsung wearable device as seen from the back cover.

[0138] FIG. 11 illustrates an exemplary functional diagram for measuring BIA signals in a wearable device 201 of the present invention, which should not be construed as limiting.

[0139] The wearable device 201 is configured to determine the user's body impedance parameters and their derivatives, for example, the magnitude (Z) of the user's body impedance modulus, the phase angle (ϕ) of the user's body impedance at certain frequencies (at least at two frequencies within 20 -40 kHz and 200-300 kHz respectively), total body fluid (TBW), intracellular fluid (ICW) to extracellular fluid (ECW) ratio, and correct the error in bioimpedance values caused by electrical signal leakage due to parasitic component (component).

[0140] In FIG. 11 illustrates a bioimpedance measurement circuit including a bioimpedance measurement unit 218 and a processor 203 of the wearable device 201. Additionally, the bioimpedance measurement unit 218 may include a separate processor. More specifically, referring to FIG. 11, the wearable device 201 includes a biocontact circuit 235, a current-voltage measurement module 240, a current-voltage circuit configuration module 250, and a processor 203. At least one of the biocontact circuit 235, a current-voltage measurement module 240, and a processor 203. voltage or current-voltage loop configuration module 250 may be an element (eg, a biometric sensor) included in the bioimpedance measurement unit 218 of FIG. 10A.

[0141] According to various embodiments, biocontact circuit 235 may include four electrodes 251, 252, 253, and 254, four I/O ports 236, 237, 238, and 239, and four elements 231, 232, 233, and 234 scheme.

[0142] Electrodes 251, 252, 253 and 254 are located on the surface of the body of the wearable device 201 to come into contact with the biological body, and thus can be electrically coupled to each other through the biological body. The first electrode 251 may be electrically coupled to the first port 236 through the first circuit element 231. The second electrode 252 may be electrically coupled to the second port 237 through the second circuit element 232. The third electrode 253 may be electrically coupled to the third port 238 through the third circuit element 233. The fourth electrode 254 may be electrically coupled to the fourth port 239 via the fourth circuit element 234.

[0143] Each of the circuit elements 231, 232, 233 and 234 may include electronic components (eg, capacitances and resistors) to remove a DC component of current from the electrical signal flowing from the corresponding port to the corresponding electrode (or vice versa). Each of the circuit elements 231, 232, 233 and 234 may have Z _S1 , Z _S2 , Z _S3 and Z _S4 as impedance components specified in the circuit design, and these impedance components may be called characteristic impedances. Impedance components Z _C1 , Z _C2 , Z _C3 and Z _C4 may occur between the biological body and the electrodes 251, 252, 253 and 254, respectively, and may be referred to as contact impedances. Contact impedances can vary according to the surface condition (skin condition) of the biological body. The contact impedance may also vary depending on the frequency of the applied electrical signal. The impedance component Z _B existing between the contact impedances is the component to be received, and Z _B may be called bioimpedance. Impedance components Zp ₁ , Zp ₂ , Zp ₃ , and Zp ₄ that are not provided for in the circuit design may be parasitic between electrodes 251, 252, 253, 254 and ground (eg, the ground of wearable device 201), respectively, and the impedance components may called parasitic impedances. Electrical signals (eg, current and/or voltage) may leak from the biocontact circuit 235 to ground due to parasitic components, and as a result, an error in the measured bioimpedance may occur due to the electrical signal leakage. The parasitic impedance value can be obtained using an impedance analysis device.

[0144] According to various embodiments, current-voltage measurement module 240 may include a power supply port 245, a current measurement port 246, a first voltage measurement port 247, a second voltage measurement port 248, an AC signal generator 241, an ammeter 242 and voltmeter 243.

[0145] Power port 245 and current sensing port 246 may be electrically coupled to biocontact circuit 235 via current-voltage loop configuration module 250.

[0146] The AC signal generator 241 may generate an electrical signal and may supply the electrical signal to biocontact circuit 235 via power port 245.

[0147] Ammeter 242 may receive an electrical signal from biocontact circuit 235 through current measurement port 246, measure the current of the received electrical signal, and transmit the current measurement value to processor 203.

[0148] The first voltage sensing port 247 and the second voltage sensing port 248 may be electrically coupled to the biocontact circuit 235 via a current-voltage loop configuration module 250 .

[0149] Voltmeter 243 may measure the voltage between the first voltage measurement port 247 and the second voltage measurement port 248 and may transmit the voltage measurement value to the processor 203.

[0150] According to various embodiments, a current-voltage circuit configuration module (hereinafter referred to as a circuit configuration module) 250 may electrically connect ports 236, 237, 238, and 239 of biocontact circuit 235 to a current-voltage measurement module 240 under the control of processor 203.

[0151] According to various embodiments, processor 203 may control circuit configuration module 250 to configure a current path established from one of the electrodes 251, 252, 253, 254 to the other electrode. The processor 203 may control the circuit configuration module 250 to configure a voltage circuit established from one of the electrodes 251, 252, 253 and 254 to the other electrode.

[0152] The processor 203 may control the circuit configuration module 250 to configure the voltage circuit such that one of the two voltage circuit electrodes matches one of the two current circuit electrodes, wherein said other of the two voltage circuit electrodes is different from said other of the two circuit electrodes. current, and the bioimpedance Z _B is not included in the voltage circuit. For example, the processor 203 may configure the current-voltage circuit in the biocontact circuit 235 such that the first electrode 251 is included in the current circuit and the voltage circuit, and the bioimpedance Z _B is not included in the voltage circuit.

[0153] The processor 203 may receive from the ammeter 242 or voltmeter 243 a current or voltage value measured in a state in which the current-voltage circuit constitutes the biocontact circuit 235. The processor 203 may measure the impedance of the first electrode 251 (including an error caused by parasitic impedance Zp ₁ between the first electrode 251 and ground) in the current/voltage circuit by using the accepted current/voltage values. Processor 203 may measure the impedances of the remaining electrodes 252, 253, and 254 in the same manner as described above.

[0154] According to an embodiment, processor 203 may provide a first control signal (eg, bioimpedance switch OFF, polarity switch OFF, and current-voltage switch OFF) to circuit configuration module 250 to measure the contact impedance of the second electrode 252. For example, in the case of "bioimpedance ON OFF", the bioimpedance Z _B may not be included in the voltage circuit. When the polarity switching signal changes from the OFF state to the ON state, the start electrode and the end electrode may change places in the current circuit. For example, in a state in which a current path is established from the first electrode 251 to the second electrode 252, if the polarity switching signal changes from the OFF state to the ON state, the current path may change to a current path installed from the second electrode 252 to the first electrode 251. When The current-voltage switching signal changes from the OFF state to the ON state, the starting electrode and the ending electrode can be swapped in the voltage circuit. For example, in a state in which the voltage circuit is installed from the fourth electrode 254 to the second electrode 252, if the current-voltage switching signal changes from the OFF state to the ON state, the voltage circuit may change from the voltage circuit installed from the second electrode 252 to the fourth electrode 254 The circuits installed according to various combinations of ON/OFF bioimpedance switching, ON/OFF polarity switching and ON/OFF current-voltage switching can be summarized as shown in Table 2 below.

[0155] Circuit configuration module 250 may connect first port 236 to recording port 245 and connect second port 237 to port 246 to measure current in response to the first control signal. Accordingly, in the biocontact circuit 235, a first current circuit may be formed from the first electrode 251 to the second electrode 252. The circuit configuration module 250 may connect the fourth port 239 to the first voltage measurement port 247 and connect the second port 237 to the second measurement port 248 voltage in response to the first control signal. Accordingly, biocontact circuit 235 may be formed by a first voltage circuit extending from fourth electrode 254 to second electrode 252. Processor 203 may receive from ammeter 242 and voltmeter 243 current/voltage values measured in the state in which biocontact circuit 235 constitutes the first circuit. current-voltage, including a first current circuit and a first voltage circuit. The processor 203 may measure the impedance of the second electrode 252 (including the error caused by the parasitic impedance Zp ₂ between the second electrode 252 and ground) by using the received current/voltage values.

[0156] The processor 203 may vary the current-voltage circuit to measure the contact impedance of the fourth electrode 254. For example, the processor 203 may provide a second control signal (eg, bio-impedance switch OFF, polarity switch OFF, and current-voltage switch ON) to the module 250circuit configuration. Circuit configuration module 250 may connect third port 238 to power port 245, connect fourth port 239 to current measurement port 246, and connect second port 237 to first voltage measurement port 247, and connect fourth port 239 to second voltage measurement port 248. response to the second control signal. Accordingly, in the biocontact circuit 235, a second current-voltage circuit may be formed that includes a second current circuit installed from the third electrode 253 to the fourth electrode 254, and a second voltage circuit installed from the second electrode 252 to the fourth electrode 254. Processor 203 may receive measured current/voltage values from ammeter 242 and voltmeter 243 in a state in which biocontact circuit 235 constitutes a second current-voltage circuit. The processor 203 may measure the impedance of the fourth electrode 254 (including the error caused by the parasitic impedance Zp ₄ between the fourth electrode 254 and ground) by using the received current/voltage values.

[0157] The processor 203 may reverse the polarity of the current in order to measure the contact impedance of the first electrode 251. For example, the processor 203 may provide a third control signal (eg, bioimpedance switch OFF, polarity switch ON, and current-voltage switch OFF) to the configuration module 250 chains. Circuit configuration module 250 may connect second port 237 to power port 245, connect first port 236 to current measurement port 246, and connect third port 238 to first voltage measurement port 247, and connect first port 236 to second voltage measurement port 248. response to the third control signal. Accordingly, a third current-voltage circuit may be formed in the biocontact circuit 235, which includes a third current circuit installed from the second electrode 252 to the first electrode 251, and a third voltage circuit installed from the third electrode 253 to the first electrode 251. Processor 203 can receive from ammeter 242 and voltmeter 243 current/voltage values measured in a state in which the third current-voltage circuit constitutes the biocontact circuit 235. The processor 203 may measure the impedance of the first electrode 251 (including the error caused by the parasitic impedance Zp ₁ between the first electrode 251 and ground) by using the received current/voltage values.

[0158] The processor 203 may change the current polarity and the current-voltage circuit in order to measure the contact impedance of the third electrode 253. For example, the processor 203 may transmit a fourth control signal (e.g., bio-impedance switch OFF, polarity switch ON, and current-voltage switch ON ) to the circuit configuration module 250. In response to the fourth control signal, processor 203 may connect fourth port 239 to power port 245, connect third port 238 to port 246 to sense current, connect first port 236 to first port 247 to sense voltage, and connect third port 238 to second port 248 to voltage measurements. Accordingly, a fourth current-voltage circuit may be formed in the biocontact circuit 235, which includes a fourth current circuit installed from the fourth electrode 254 to the third electrode 253, and a fourth voltage circuit installed from the first electrode 251 to the third electrode 253. Processor 203 can receive from ammeter 242 and voltmeter 243 current/voltage values measured in a state in which the fourth current-voltage circuit constitutes the biocontact circuit 235. The processor 203 may measure the impedance of the third electrode 253 (including the error caused by the parasitic impedance Z _P3 between the third electrode 253 and ground) by using the received current/voltage values.

[0159] 235 According to various embodiments, processor 203 may correct the impedance measurement value by using parasitic impedances Zp ₁ , Zp ₂ , Zp ₃ , and Zp ₄ and characteristic impedances Zs ₁ , Zs ₂ , Zs ₃ , and Zs ₄ (e.g., eliminating error measured value caused by parasitic impedance) to obtain the impedances of Z _C1 , Z _C2 , Z _C3 and Z _C4 contacts.

[0160] According to various embodiments, the processor 203 may control the circuit configuration module 250 to configure the current circuit and the voltage circuit such that one of the two voltage circuit electrodes is the same as one of the two current circuit electrodes, the other of the two voltage circuit electrodes is different from the other of the two electrodes of the current circuit, and the bioimpedance Z _B was included in both the current circuit and the voltage circuit. The processor 203 may receive from the ammeter 242 and voltmeter 243 current/voltage values measured in a state in which the biocontact circuit 235 constitutes a current-voltage circuit. The processor 203 may calculate the bioimpedance Z _B by using the received current/voltage values, the characteristic impedances Z _S1 , Z _S2 , Z _S3 and Z _S4 , the parasitic impedances Zp ₁ Z _P2 , Z _P3 and Z _P4 , and the impedances Z _C1 , Z _C2 , Z _C3 and Zc ₄ pins.

[0161] According to an embodiment, the processor 203 may configure the current-voltage circuit in the biocontact circuit 235 such that the second electrode 252 and the bioimpedance Z _B are included in the current circuit and the voltage circuit. For example, processor 203 may transmit a fifth control signal (eg, bioimpedance switch ON, polarity switch OFF, and current-voltage switch OFF) to circuit configuration module 250. In response to the fifth control signal, circuit configuration module 250 may connect first port 236 to power port 245, connect second port 237 to current sensing port 246, connect third port 238 to first voltage sensing port 247, and connect second port 237 to the second port. 248 for voltage measurement. Accordingly, in the bio contact circuit 235, a fifth current-voltage circuit may be formed, which includes a fifth current circuit installed from the first electrode 251 to the second electrode 252, and a fifth voltage circuit installed from the third electrode 253 to the second electrode 252. Processor 203 can receive from ammeter 242 and voltmeter 243 current/voltage values measured in a state in which biocontact circuit 235 constitutes a fifth current-voltage circuit. The processor 203 may calculate the first bioimpedance Z _B1 by using the current/voltage value, the characteristic impedance value, the parasitic impedance value, and the contact impedance value, which are measured using the fifth current-voltage circuit.

[0162] According to an embodiment, the processor 203 may change the current-voltage path such that the fourth electrode 254 and the bioimpedance Z _B are included in the current path and the voltage path. For example, processor 203 may transmit a sixth control signal (eg, bioimpedance switch ON, polarity switch OFF, and current-voltage switch ON) to circuit configuration module 250. The circuit configuration module 250 may control, in response to the sixth control signal, the circuit configuration module 250 to connect the third port 238 to the power supply port 245, connect the fourth port 239 to the current sensing port 246, connect the first port 236 to the first voltage sensing port 247, and connect a fourth port 239 with a second port 248 for voltage measurement. Accordingly, a sixth current-voltage circuit may be formed in the biocontact circuit 235, which includes a second current circuit installed from the third electrode 253 to the fourth electrode 254, and a sixth voltage circuit installed from the first electrode 251 to the fourth electrode 254. Processor 203 can receive from ammeter 242 and voltmeter 243 current/voltage values measured in a state in which the sixth current-voltage circuit constitutes the biocontact circuit 235. The processor 203 may calculate the second bioimpedance Z _B2 by using the current/voltage value, the characteristic impedance value, the parasitic impedance value, and the contact impedance value, which are measured using the sixth current-voltage circuit.

[0163] According to an embodiment, the processor 203 may change the polarity of the current so that the first electrode 251 and the bioimpedance Z _B are included in the current path and the voltage path. For example, processor 203 may transmit a seventh control signal (eg, bioimpedance switch ON, polarity switch ON, and current-voltage switch OFF) to circuit configuration module 250. The circuit configuration module 250 may control, in response to the seventh control signal, the circuit configuration module 250 to connect the second port 237 to the power port 245, connect the first port 236 to the current sensing port 246, connect the fourth port 239 to the first voltage sensing port 247, and connect first port 236 with second port 248 for voltage measurement. Accordingly, a seventh current-voltage circuit may be formed in the biocontact circuit 235, which includes a third current circuit installed from the second electrode 252 to the first electrode 251, and a seventh voltage circuit installed from the fourth electrode 254 to the first electrode 251. Processor 203 can receive from ammeter 242 and voltmeter 243 current/voltage values measured in a state in which the seventh current-voltage circuit constitutes the biocontact circuit 235. The processor 203 may calculate the third bioimpedance Z _B3 by using the current/voltage value, the characteristic impedance value, the parasitic impedance value, and the contact impedance value, which are measured by the seventh current-voltage circuit.

[0164] According to an embodiment, the processor 203 can change the polarity of the current and the current-voltage path so that the third electrode 253 and the bioimpedance Z _B are included in the current path and the voltage path. For example, processor 203 may transmit an eighth control signal (eg, bioimpedance ON, polarity ON, and current-voltage ON) to circuit configuration module 250. In response to the eighth control signal, circuit configuration module 250 may connect fourth port 239 to power port 245, connect third port 238 to current sensing port 246, connect second port 237 to first voltage sensing port 247, and connect third port 238 to the second port. 248 for voltage measurement. Accordingly, an eighth current-voltage circuit may be formed in the biocontact circuit 235, which includes a fourth current circuit installed from the fourth electrode 254 to the third electrode 253, and an eighth voltage circuit installed from the second electrode 252 to the third electrode 253. Processor 203 can receive from ammeter 242 and voltmeter 243 current/voltage values measured in a state in which the eighth current-voltage circuit constitutes the biocontact circuit 235. The processor 203 may calculate the fourth bioimpedance Z _B4 by using the current/voltage value, the characteristic impedance value, the parasitic impedance value, and the contact impedance value that are measured by the eighth current-voltage circuit.

[0165] The first to eighth current-voltage circuits that may be configured in biocontact circuit 235 as described above may be summarized in Table 2 below.

[0166] According to an embodiment of the present invention, the processor 203 is configured to receive the at least two PPG signals from the at least one PPG sensor 208, store them in the storage device 212, and load them from the storage device 212 to perform operations processing.

[0167] The processor 203 is configured to filter the at least two PPG signals of different wavelengths from motion artifacts, separate the at least two PPG signals of different wavelengths into AC and DC components of the time series, and convert these time series (time domain signal) into the frequency domain or time-frequency domain and then extracting their amplitude-phase harmonic characteristics, wherein the transformation of said time series into the frequency domain may comprise a Fourier transform or a Hilbert-Huang transform, and the transformation of said time series into the time-frequency domain may contain a wavelet transform or a bilinear time-frequency distribution.

[0168] The processor 203 is configured to receive additional data (eg, data from said additional sensors of the wearable device 201), store it on the storage device 212 of the wearable device 201, and load it from the storage device 212 to perform operations, if necessary.

[0169] The processor 203 of the wearable device 201 is configured to transmit information about the values of the user's blood parameters determined based on the readings of the sensors 208 to the output device 210 to inform the user.

[0170] The processor 203 is configured to load data received by the communication module 213 into the storage device 212, and/or load data from the storage device 212 into the communication module 213 for transferring it to an external device (e.g., devices 225, 226, server 227 or cloud storage 229).

[0171] The battery 204 is configured to supply power to at least one component of the wearable device 201.

[0172] With reference to FIG. 10B illustrates that the at least one PPG sensor 208 of the wearable device 201 is located on its rear surface. Thus, during operation, the radiation surface of the PPG sensor comes into contact with the user’s wrist. The wearable device 201 may include multiple PPG sensors.

[0173] The at least one PPG sensor 208 is configured to measure at least two PPG signals with different wavelengths. In the embodiment of the present invention shown in FIG. 10B, the at least one PPG sensor 208 is located on the rear surface 215 of the housing 202 (the surface facing the user's skin). In one embodiment of the present invention (not shown), the wearable device 201 may include two PPG sensors 208, each of which contains one radiation source 236 and one radiation receiver 237, the radiation sources 236 of the two PPG sensors emitting light of different wavelengths. . In this embodiment of the present invention, two PPG sensors 208 are capable of capturing at least two PPG signals of different wavelengths. In the embodiment of the present invention shown in FIG. 10B, the wearable device 201 includes one PPG sensor 208 containing two radiation sources 236 and two radiation receivers 237, the radiation sources 236 emitting light of different wavelengths. In this embodiment of the present invention, one PPG sensor 208 is capable of capturing at least two PPG signals of different wavelengths. In an embodiment of the present invention, the radiation source 236 may comprise a light-emitting diode (LED). Photobarriers may be provided between radiation sources and/or receivers to reduce noise and eliminate crosstalk.

[0174] Input device 209 is configured to receive data that can be used by another component (eg, processor 203) of wearable device 201, input from externally (eg, a user of wearable device 201). In one embodiment of the present invention, input device 209 may be configured to input a user profile. The processor 203 of the wearable device 201 may receive the user profile from the input device 209, store the user profile in the storage device 212, read it from the storage device 212, and use it as needed. The wearable device 201 may also provide voice input and output.

[0175] User profile data may include gender, age, height, weight of the user. Also, the input and output device may include a screen for displaying information, for example, a certain hemoglobin concentration. The screen may be configured to use an on-screen keyboard for data entry.

[0176] The output device 210 may be configured to display information to the user on a first side 214 of the wearable device 201. The output device 210 may include a display 211 that may be configured to display output information and located on the first side 214 of the wearable device 201. Additionally, the display 211 may be configured to use an on-screen keyboard to enter data, such as a user profile.

[0177] Storage device 212 in a preferred embodiment may be configured to store:

databases with sets of blood structure characteristics, including characteristics of the molecular and cellular structure of blood, compared with various blood parameters measured by laboratory methods;

user profile data;

sets of measurement data containing characteristics of the molecular and cellular structure of blood and including:

the magnitude of the first harmonic (f ₁ ), the magnitude of the amplitude-phase characteristics (t _λ ⁰ ) of the harmonics of at least two PPG signals with different wavelengths,

at least one parameter of the user's body impedance, such as a magnitude (Z) of the user's body impedance modulus, a phase angle (ϕ) of the user's body impedance at different frequencies, total body fluid (TBW), intracellular fluid (ICW) to extracellular fluid (ECW);

dates of collection of each set of measurement data;

collection times of each set of measurement data;

blood parameters determined by the processor for each set of measurement data, etc.

[0178] Also, the memory device 212 may store various additional data, such as that collected from additional sensors of the wearable device 201. In addition, the memory device 212 may store various instructions that, when executed on the processor 203, cause the processor 203 to control components of the wearable device 201 associated with the processor 203 and perform various data processing or calculations.

[0179] A communication module 213 may be used to transmit data from or to the wearable device. According to an embodiment, communications module 213 may include a wireless communications module 223 (e.g., a cellular communications module, a short-range wireless communications module, or a global navigation satellite system (GNSS) communications module) or a wired communications module 224 (e.g., a local area network communications module (LAN) or power line communication module). Thus, the communication module 213 may support establishing a wireless communication channel between the wearable device 201 and an external device (eg, device 225, device 226, server 227, or cloud storage 228) and communicating through the established communication channel. Communications module 213 may include one or more communications processors that operate independently of processor 203 and support direct (eg, wired) communications or wireless communications. A respective one of these communication modules may communicate with an external electronic device via a first network 228 (e.g., a short-range communication network such as Bluetooth™, wireless (Wi-Fi), or infrared data transmission (IrDA)) or a second network 229 (eg, a long-distance communications network, such as a cellular network, the Internet, or a computer network, such as a local area network or wide area network (WAN)). These various types of communication modules may be implemented as a single component (eg, a single chip) or may be multi-component (eg, multiple chips).

[0180] In FIG. 12 shows a graph of the deviations of hemoglobin values measured by various non-invasive devices of the prior art for a plurality of volunteers (a device from Massimo Corp. or a wearable device from Samsung) from hemoglobin levels measured by a laboratory method using venous blood sampling (reference values), depending on the average value these pairwise measurements. The data is based on 840 measurements performed on 140 volunteers, including men and women with different hemoglobin levels. These data were collected at the medical center, and the data collection protocol was reviewed and approved by the ethical commission of Saratov State Medical University (Russia). As can be seen from Fig. 12, the error in measuring hemoglobin levels with non-invasive devices increases at high and low hemoglobin levels, and at low hemoglobin levels, non-invasive devices produce overestimated measurement results, and at high hemoglobin levels, underestimated results compared to those obtained from laboratory analysis of venous blood samples.

[0181] In FIG. 13 shows graphs of hemoglobin levels measured by these prior art devices for a variety of volunteers with low hemoglobin (upper graph) and with normal hemoglobin (lower graph). Here, solid circles show reference data for hemoglobin levels obtained by laboratory methods. It is obvious that with a normal hemoglobin level, the measurement results with non-invasive devices of the state of the art are quite correct, however, when measuring on volunteers with low hemoglobin levels, the measurement error increases significantly, and the values obtained by non-invasive devices are overestimated. A similar situation arises when taking measurements on volunteers with high hemoglobin levels, and in this case the values obtained by non-invasive devices are underestimated.

[0182] The problem is that it is people with high and low hemoglobin who need frequent and sometimes continuous monitoring of their levels. Thus, there is a clear need to adjust hemoglobin levels measured by non-invasive devices, especially at high and low levels of hemoglobin in the blood of users of such devices.

[0183] In FIG. 14 shows the key features (KO) of measuring blood parameters in accordance with the present disclosure. As discussed above with the example of hemoglobin determination, the use of optical methods alone does not provide the ability to accurately measure blood parameters at extreme values. Therefore, to more fully take into account the structure of the blood, bioimpedance analysis is also used. Thus, the first key feature (CO1) of the present disclosure is the simultaneous use of bioimpedance (BIA) and photoplethysmography (PPG) sensors, with the help of which signals characterizing the molecular and cellular structure of blood are obtained. This approach makes it possible to comprehensively describe the biophysical state of blood non-invasively: characteristics of the cellular structure using bioimpedance signals, characteristics of the molecular structure using optical signals. The result of CO1 is the registration of signals from BIA and PPG sensors for further determination of the characteristics of the molecular and cellular structure of blood.

[0184] The second key feature (CO2) of the present disclosure is the extraction of molecular and cellular characteristics of blood from the signals recorded by said sensors to compile a database. The training data set is collected from a diverse population with a wide range of variations in blood parameters. The database includes a set of characteristics of the molecular and cellular structure of blood, determined using BIA and PPG sensors, compared with reference blood parameters determined by laboratory analysis methods. In this way a database is formed.

[0185] The third key feature (KO3) of the present disclosure is the accurate estimation of Hb and other blood parameters using a predictive model that is built and trained using a compiled database. Using a predictive model, blood parameters can be accurately estimated for users not included in the training dataset.

[0186] As a result, the present disclosure makes it possible to automatically, accurately, and noninvasively estimate blood parameters (e.g., Hb, HCT, tEV, MCHC, BV, PV, etc.) over the entire measurement range, including extreme cases of low or high values. blood parameter.

[0187] FIG. 15 shows CO1 of the present disclosure. First, using a wearable device with built-in BIA and PPG sensors, electrical BIA signals characterizing the cellular structure of the blood and optical PPG signals characterizing the molecular structure of the blood of a particular user are simultaneously recorded. Estimating the value of a blood parameter, for example, the level of hemoglobin, only using an optical sensor is carried out with an error, especially with marginal values, therefore, additional consideration is required for the characteristics of the cellular structure of the blood, for example, the amount of plasma, extra- and intracellular fluid, spatial distribution of hemoglobin, etc. .

[0188] Next, the characteristics of the molecular and cellular structure of blood are extracted from the signals recorded by the BIA and PPG sensors and the current state of the user’s blood structure is determined. Thus, a specific set of blood properties, containing characteristics of the molecular and cellular structure of blood, corresponds to a specific blood parameter value (BPV), for example, Hb, HCT, tEV, MCHC, BV, PV, etc.

[0189] In FIG. 16 shows KO2 of the present disclosure, which consists of compiling a database for non-invasive, personal monitoring of blood parameters. In the present invention, this is achieved as follows. As a result of machine learning of a neural network, a predictive model is created in advance. To train a predictive model that can determine various blood parameters, a database is compiled. To do this, as has been described with reference to FIG. 15, for each of a plurality of wearable device users, a specific set of blood properties is obtained, containing characteristics of the molecular and cellular structure of blood, which corresponds to a specific blood parameter value (BPV). Additionally, for each of the plurality of users, the reference value of the blood parameter is determined by the reference method, for example, by performing a blood test by some laboratory method. After this, the corresponding sets of blood properties of multiple users determined using a wearable device are correlated with the reference values of the blood parameter determined by the laboratory method, and a database is compiled in which for each user the set of blood properties includes the state of the blood structure recorded by PPG and BIA sensors, along with the user profile and blood parameter reference value. In this way, a database is compiled in which, as shown in FIG. 16, each set of blood properties (A, B,...N), i.e. The molecular and cellular structure of the blood, encoded in the characteristics, corresponds to the reference value(s) of the blood parameter (BPV _A , BPV _B ,…BPV _N ). Thus, the database contains all possible combinations of sets of blood properties with specific reference values of blood parameters. The database compiled in this way is used for machine learning of the predictive model. The predictive model used in the processor of the wearable device provides determination of the user's blood parameters based on sets of properties characterizing the molecular and cellular structure of blood obtained by processing PPG and BIA signals for a specific user of the wearable device.

In this way, errors made during non-invasive measurement of a blood parameter can be leveled out.

[0190] In FIG. 17 and 18 show OB3 of the present disclosure, which is to accurately estimate Hb and other blood parameters using a predictive model. Building a predictive model as shown in FIG. 17 are carried out on the basis of a pre-compiled database containing sets of characteristics of the molecular and cellular structure of blood, correlated with reference data, i.e. values of blood parameters measured by laboratory methods. When collecting basic data, for example, when determining hemoglobin concentration, a laboratory analysis of capillary blood is used using a colorimetric method based on hemoglobinazide analysis. To build a predictive model, any suitable machine learning method can be used, for example, a machine learning regression model, namely the gradient descent method and k-fold cross-validation to evaluate the prediction performance. At the same time, the model error is minimized using the database and the best model parameters w _best are found.

[0191] The data obtained clearly demonstrate that blood parameters, in particular hemoglobin concentration, as shown in FIG. 18, the blood level of the user of the wearable device can be predicted based on at least a set of measurement data taken from the sensors of the wearable device, with high accuracy relative to the readings obtained by a laboratory method. It is obvious that the accuracy of forecasting will increase both as the forecasting model is refined and as information with the results of measurements on users is accumulated in the database. Thus, it is expected that the wearable device of the invention will be able to determine the blood parameters of users with high accuracy (within 1-3%).

[0192] The solution to the problem of predicting the values of the user's blood parameters is based on identifying empirical patterns in the training data placed in a common database using a machine method.

[0193] In an embodiment of the present invention, a predictive model is created for a wearable device located on the wrist, since training of the predictive model was based on data captured by sensors of the wearable device located on the wrist. In other embodiments of the present invention, it is possible to train a predictive model based on placement of the wearable device on other parts of the body, such as a finger. The predictive model described herein may be implemented as software including one or more instructions that may be executed by the processor 203 of the wearable device 201 or by external processors/processing devices.

[0194] The above-described process for training the predictive model used profile data from other users (test subjects), measurement data sets previously measured from other users, and reference data sets previously measured by a laboratory method, as described in detail above.

[0195] As discussed below, the database may be continually updated and, in addition, the prediction model may be refined through machine learning.

In the process of machine learning, methods for selecting and processing characteristics and corresponding discrete frequencies can be used:

- in the Relief-F method, the vector of weights of features (characteristics) is calculated and normalized, and then features are selected whose weight exceeds the value of a given threshold;

- the Correlation-based Feature Selection (CFS) method combines an evaluation formula with an appropriate correlation measure and a heuristic search strategy;

- the Fast Correlation Based Filter method begins to work with the full set of features, uses a measure of symmetric uncertainty to determine dependencies between features and allows you to select a subset by searching and sequentially eliminating uninformative features;

- the Sequential Forward Feature Selection (SFFS) method at each iteration adds to the set a feature that provides the best recognition efficiency for a given iteration;

- the mutual information method determines the nonlinear correlation dependence instead of calculating the Pearson correlation “feature-feature” and “feature-label”;

- basic machine learning (artificial intelligence) algorithms;

- as a working solution, a combination of the specified regression methods and any derivative regression methods, which are based on the basic algorithm, can be used:

- decision trees (Decision Tree) / random forest (Rnom Forests);

- support vector machines (Support Vector Machines);

- Linear Analysis;

- deep learning methods - artificial neural networks.

[0196] The advantageous effects of using a machine learning method are the ability to determine blood parameters for a user whose data is not included in the database and improve the accuracy of blood parameter determination.

[0197] Once the predictive model is built, it can be used to predict the values of blood parameters such as, for example, hemoglobin (Hb), hematocrit (HCT), plasma volume (PV), blood volume (BV), total red blood cell volume (tEV) ), average hemoglobin concentration in erythrocytes (MCHC), etc. Moreover, according to the present disclosure, data related to both the molecular and cellular structure of blood are used to predict the properties of blood. As a result, a more accurate assessment of blood parameters (cHb, tHb, NCT, PV, BV, tEV, MCHC, etc.) is possible than in traditional methods of the state of the art. As an example, in FIG. 18 shows the results of hemoglobin level measurement by a wearable device containing only an optical (PPG) sensor (upper graph) and a wearable device containing both a PPG sensor and a BIA sensor of the present invention (lower graph). In the graphs of Fig. 18, the x-axis shows the average between two hemoglobin values, one of which is determined by a laboratory method and the other by a wearable device of the prior art, and the y-axis shows the difference between these two values. From the top graph of Fig. 18 it can be seen that when determining the hemoglobin level using the traditional non-invasive method, when the user's hemoglobin is low, the determined values are overestimated, while when the user has high hemoglobin, the determined values are underestimated. Thus, the marginal values of the hemoglobin level in this case are determined incorrectly (MAE indicator = 12.37, MAE (Mean Absolute Error) is the average absolute error, which is a measure of errors between paired measurements of the same phenomenon, that is, in this case, between actual values of hemoglobin concentration measured by a laboratory method and predicted values obtained using a wearable device; MAE is calculated as the sum of absolute errors divided by the sample size). From the lower graph of Fig. 18, it can be seen that the use of a wearable device according to the present invention significantly reduces errors in determining high and low hemoglobin (MAE value = 6.67).

[0198] To summarize the above, a method for determining blood parameters is carried out using a wearable device according to the present invention as follows:

- using PPG and BIA sensors, optical and electrical signals are simultaneously recorded, respectively, i.e. photocurrent and impedance, for multiple users;

- collect a data set of reference blood parameter values (BPV), determined using a blood test, for a plurality of users;

characteristics of the molecular and cellular structure of blood are extracted from the signals recorded by the mentioned sensors and compared with different values of blood parameters (states of blood structure) defined for a variety of users;

- compile a database including i) reference values of blood parameters; ii) characteristics of the molecular structure of blood; iii) blood cellular characteristics and, optionally, iv) user phenotype data;

- construct and train a predictive model using a compiled database;

- accurately predict blood parameters, for example, hemoglobin level for new users without performing a blood test, by comparing a set of characteristics of the molecular and cellular structure of the new user's blood, obtained using the mentioned sensors of the wearable device, with sets from a pre-compiled database.

[0199] Thus, the present invention provides the possibility of non-invasive instantaneous or continuous accurate assessment of the values of various blood parameters (for example, cHb, tHb, NCT, tEV, BV, PV, etc.), including at the edge values of the estimated parameter.

Embodiment 1

[0200] The present embodiment shown in FIG. 19 refers to the additional use of machine learning (ML) in the claimed method. As in the above-described embodiment, PPG and BIA signals are first recorded from a user with an unknown blood parameter value, for example, cHb, tHb, NCT, tEV, BV, PV, then a set of characteristics describing the molecular and cellular structure is extracted from the signals recorded by the mentioned sensors user's blood. After this, by comparing the resulting set of characteristics with the sets included in the database, the required value of the blood parameter is determined. In contrast to the above-described embodiment, a situation may arise where, when comparing the resulting set of characteristics with the sets included in the database, it will not be possible to unambiguously determine the value of the blood parameter, since the resulting set will not uniquely correspond to any set from the database. In this case, a laboratory analysis of the user’s blood is carried out and the result is entered into the database, correlating it with the obtained set of characteristics, and additionally, using the machine learning method, the predictive model is refined. Moreover, in this embodiment, user profile data such as history of previous blood parameter measurements, body weight, muscle mass, age, etc. are also entered into the database, allowing the prediction model to be further refined. The advantageous effect is improved accuracy of blood parameter determination for a user whose data is not included in the database.

Embodiment 2

[0201] The present embodiment shown in FIG. 20 refers to the additional use of information about the user's body morphology. As discussed above, the molecular structure of blood is characterized by the BIA signal detected in the user. The characteristics of such a signal are the magnitude of the body impedance (active and reactive components), the phase of the body impedance, the magnitude of the contact impedance, the amplitude of the variable frequency component and the resistance of the human body. The cellular structure of blood is characterized by the PPG signal using the waveform parameters of the detected PPG signal. In this embodiment, unlike Embodiment 1, the user profile entered into the database may contain various information, including amount of fat and muscle mass. The advantageous effect of using such user profile information is improved accuracy in determining blood parameters for a user whose data is not included in the database.

Embodiment 3

[0202] The present embodiment shown in FIG. 21 relates to the potential application of a wearable device with a local BIA sensor. In prior art wearable devices using a tetrapolar sensing circuit, two electrodes are located on the back cover side, and two electrodes are located on control buttons on the side surface of the body of the wearable device, as shown in FIG. 10B in relation to Samsung smartwatches. Thus, when measuring with a BIA sensor (measurement time is usually about 15 seconds), two electrodes are in contact with the wrist of one hand of the user, and the fingers of the other hand of the user are in contact with the electrodes on the side surface, and therefore the measuring circuit along which the current flowing includes both of the user's arms and upper body. In the present embodiment, all four electrodes of the BIA sensor may be located on the back cover of the body of the wearable device, i.e. The current path supplied by the BIA sensor approximately coincides with the path of optical signals from/to the PPG sensor, i.e. The same part of the user's body is measured. Heterogeneity in the structural elements of cells, biological tissues, organs and the entire human body, variability in molecular composition resulting from the dynamics of biochemical reactions, and the contribution of components such as ions, proteins and polarized membranes strongly influence the interpretation of the results of bioimpedance analysis. Measuring the same volume of the user's biological tissue using BIA and PPG sensors results in increased accuracy in determining the user's blood parameters, which is an advantageous effect of this embodiment. Effect of using the claimed invention

[0203] In FIG. 18 and 22 show the results of determining the hemoglobin level of wearable device users as an example. To plot the graph in Fig. 18 used 30 measurements performed on 10 users (5 men and 5 women). In this case, the upper graph in Fig. 18 is based on measurements taken by a wearable device having only a PPG sensor, and the bottom graph is based on measurements taken by a wearable device according to the invention having both a PPG sensor and a BIA sensor.

[0204] As already noted, from the top graph in Fig. 18 it is clear that when determining the hemoglobin level with a wearable device having only a PPG sensor (traditional method), when the user’s hemoglobin is low, the determined values are overestimated, while when the hemoglobin is high the determined values are underestimated. Thus, the marginal values of hemoglobin level in this case are determined incorrectly (MAE indicator = 12.37). From the bottom graph of Fig. 18 it can be seen that the use of a wearable device according to the present invention leads to a significant reduction in the error in determining high and low hemoglobin, i.e. when determining marginal values (MAE indicator = 6.67).

[0205] In FIG. Figure 22 shows graphs of changes in hemoglobin values for a user with low hemoglobin throughout the day. In addition, for this user, a blood test was performed using a laboratory method to obtain a reference (correct) value of a blood parameter, in this case, the hemoglobin level. Reference values for hemoglobin levels are marked in Fig. 22 solid circles. From the top graph of FIG. 22 it is clear that the determination of hemoglobin level when using the traditional measurement method using only the optical sensor of a wearable device is carried out with an error, and the determined values are overestimated compared to the reference values obtained by the laboratory method. At the same time, the use of a wearable device according to the present invention to determine hemoglobin leads to a significant reduction in the determination error even when measuring the edge values of the parameter (see the lower graph of Fig. 22).

[0206] Table 3, as an example, shows the measurement results of blood parameters such as hemoglobin concentration (cHb), total hemoglobin mass (total hemoglobin) (tHb), hematocrit (HBT), total blood volume (BV), plasma volume (PV), including at marginal values of parameters, in accordance with the principles of this disclosure, taking into account the molecular and cellular structure of blood for two different volunteers with different physiological parameters (volunteer No. 1: weight 68 kg, height 182 cm, gender male; volunteer No. 2: weight 48 kg, height 167 cm, gender female). Knowing the exact values of hemoglobin concentration (cHb) and hematocrit (HCT), you can calculate the average hemoglobin concentration in erythrocytes (MCHC):

MCHC=(cHb×100)/NST

Reference measurements of hemoglobin levels to determine reference (reference) values were carried out on a capillary blood sample using a hemoglobin analyzer model HemoCue Hb 201+, which has a high correlation with a laboratory blood test. Hemoglobin concentration (cHb) values obtained using HemoCue Hb 201+ for Volunteer 1 and Volunteer 2 are 148 g/L and 102 g/L, respectively.

Reference measurements of total body fluid and intracellular fluid (ICW) to extracellular fluid ratio (ECW) were obtained using the InBody 770 body composition analyzer. Total body fluid (TBW) values and intracellular to extracellular fluid ratio (ICW/ The ECW) obtained with the InBody 770 for Volunteer 1 and Volunteer 2 are 42.9 L, 1.65 and 28.3 L, 1.62, respectively. It is known from the prior art that blood plasma on average makes up 20% of the extracellular fluid. Therefore, based on the TBW and ICW/ECW, plasma volume (PV) can be calculated, which for Volunteer 1 and Volunteer 2 is 3.24 L and 2.16 L, respectively.

Examples of use of the invention

[0207] The invention can be used to instantly measure or continuously monitor blood parameters of the user of a wearable device, such as a smart watch or fitness bracelet. By continuously monitoring the user's blood counts, both short- and long-term deviations from the normal range of blood counts can be detected. Short-term deviations are not critical and can be eliminated personally by the user based on the recommendations provided. For example, with a short-term decrease in hemoglobin concentration, which most often indicates anemia, it may be recommended to change the diet, increase physical activity, spend more time in the fresh air, etc. At the same time, long-term deviations may indicate serious health problems and the need to see a doctor. In this case, the obtained data can be sent to the doctor remotely to collect a primary medical history.

[0208] As an example, when continuously monitoring the user's blood parameters, in particular hemoglobin, a machine learning algorithm analyzes the data received from the sensors and, taking into account the user's profile data, recommendations can be made on the user's lifestyle, including advice on nutrition, physical activity . Such recommendations may be issued in the following cases:

- monitoring of health status, when for many months the hemoglobin level has varied within limited limits, and then a tendency to increase/decrease in hemoglobin begins to be observed, which can be caused by fatigue, illness, etc.;

- tracking the course of the disease; in this case, during the course of the illness, the hemoglobin level decreases significantly, but tends to normal values upon recovery, so it is possible to control the recovery process;

- control of women's health; During menstruation, the hemoglobin level decreases, so it is possible to control the timing and duration of menopause; During pregnancy, hemoglobin levels are subject to sudden and frequent changes, so continuous monitoring of hemoglobin helps pregnant women establish an optimal regime of physical activity and rest; for example, with low hemoglobin, physical activity is undesirable.

[0209] Another example of the use of the invention can be monitoring the physical activity of athletes. As noted above, the main function of hemoglobin is to bind and transport oxygen to internal organs. Deviation of the Hb value from the norm leads to a decrease in endurance during sports. Low Hb leads to oxygen starvation of the muscles (hypoxia or low number of red blood cells), resulting in: lethargy, periodic dizziness, fatigue, muscle pain, shortness of breath, rapid heartbeat. High Hb leads to dehydration or a high number of red blood cells, resulting in: lethargy, nosebleeds, fatigue, muscle pain. Such conditions are difficult to tolerate, especially during physical activity. Muscles need oxygen to get enough energy, and a lack of oxygen results in the inability of fibers to contract adequately. Athletes may not immediately notice the symptoms of onset anemia, which is associated with increased endurance of the body. Therefore, it is necessary to know the hemoglobin concentration before training, and it is better to continuously monitor it. Therefore, the dynamics of changes in blood parameters is very important for planning and analyzing the training of athletes, and if there is a tendency, for example, to increase/decrease hemoglobin, personal recommendations can be given in choosing a sports training regimen, which is important for beginner athletes, athletes after a long break from training, triathletes, and female athletes and during training at altitude.

[0210] Thus, the effectiveness of the user's physical activity is partly determined by the high oxygen-carrying capacity of the blood, analysis of blood parameters helps in determining the cause of fatigue, overtraining conditions and issuing recommendations for quick recovery and effective training, and also helps in proper nutrition management.

[0211] An athlete using a smartwatch switches it to a mode for measuring a blood value, such as hemoglobin. Given continuously incoming data, the user's profile and geolocation, a machine learning algorithm analyzes the data from the hemoglobin sensor, which is then provided to the user in the form of a hemoglobin concentration reading. Based on the controlled hemoglobin concentration, personalized training recommendations can be provided, such as mode, duration, load level, etc.

[0212] In addition, blood count analysis is important for monitoring a person's overall health. For example, oxygen deprivation has consequences for all people, since a lack of oxygen in the blood can negatively affect brain function. Specifically, hematocrit, hemoglobin level in the blood, and total hemoglobin mass characterize the total amount of oxygen that can be delivered to the body's tissues, while total red blood cell volume and plasma volume characterize the total amount of oxygen that can be transported by the blood. Therefore, high values of these indicators allow the body to redistribute oxygen to the organs and tissues most in need while maintaining the main supply of oxygen to less active organs and tissues.

[0213] As an example, the dynamics of the relative (%) changes in blood volume (middle line), plasma volume (upper line) and red blood cell volume (lower line) are assessed depending on the number of days of training, as shown in FIG. 23. Each point represents the average change recorded in a group of subjects from one study. The data were based on 18 studies that reported all three vascular volumes mentioned (V. A. Convertino, “Blood Volume Response to Physical Activity and Inactivity”), Symposium on blood volume in clinical medicine, volume 334, topic 1, cc. 72-79, July 2007).

[0214] As shown in Fig. 24, the optical properties of blood cells are determined using a PPG sensor, which is sensitive to the amount of Hb, melanin, leukocytes, lymphocytes, but has low sensitivity to plasma and blood components such as red and white blood cells, platelets, while electrical properties are determined using a BIA sensor, which, on the contrary, has high sensitivity to plasma and blood components and low sensitivity to the amount of Hb, melanin, leukocytes, and lymphocytes. Therefore, the content of Hb, melanin, leukocytes, and lymphocytes can be assessed using the corresponding optical absorption spectra in the visible range. At the same time, by varying the frequency of the current, it is possible to estimate the amount of extra- and intracellular fluid and the distribution of fluid in the body.

[0215] Thus, the claimed invention provides a qualitative determination of blood parameters through the simultaneous use of PPG and BIA sensors.

[0216] Additional advantages of using the claimed invention are the low power consumption of wearable devices, which means a long period of continuous monitoring of blood parameters, and unobtrusive interaction with the user.

[0217] The present invention enhances the functionality of the Samsung Health application in smart watches by providing additional parameters for a comprehensive analysis of the user's health status. Smart watches can provide the following indicators: daily fluctuations, current values, continuous measurement results throughout the day, average values for a given period of hemoglobin concentration (cHb), total hemoglobin mass (tHb), hematocrit (HCT), total red blood cell volume (tEV), blood volume (BV), plasma volume (PV) and other blood parameters.

[0218] Although the invention has been described with certain illustrative embodiments, it should be understood that the invention is not limited to these specific embodiments. On the contrary, the spirit of the invention is intended to include all alternatives, modifications and equivalents that may be included within the spirit and scope of the claims.

[0219] In addition, the invention includes all equivalents of the claimed invention, even if the claims change during the course of examination.

Claims (86)

1. A wearable device with the function of determining at least one parameter of the user’s blood, containing: at least one photoplethysmography (PPG) sensor configured to irradiate the user's tissue with radiation of at least two different wavelengths and detect at least two optical PPG signals at the at least two different wavelengths in a reflection mode, a bioimpedance (BIA) sensor containing two pairs of electrodes and configured to pass alternating current through the user's body and record corresponding electrical signals, wherein the wearable device is configured to: process the signals registered by the mentioned sensors, obtain from them the characteristics of the molecular and cellular structure of the blood and form a set of characteristics of the blood structure that determine the state of the blood structure; when processing PPG signals and obtaining characteristics of the molecular structure of blood, convert the recorded PPG signals from the time domain to the frequency domain, select from all PPG signals the most significant signal, characterized by the highest signal-to-noise ratio, the highest amplitude and/or the least noise, and use it as a reference signal, extract harmonics from the reference signal and use them as coordinates, decompose all converted PPG signals according to the mentioned coordinates, determine the value of the fundamental harmonic and the magnitude of the amplitude-phase characteristics of other harmonics of at least two PPG signals with different wavelengths and describe the converted PPG signals relative to harmonics to obtain characteristics of the molecular structure of blood; when processing BIA signals and obtaining characteristics of the cellular structure of blood, register the voltage drop across the electrodes to obtain at least one parameter of the user's body impedance, such as the value of the user's body impedance modulus, the phase angle of the user's body impedance at different frequencies; And determine the value of at least one user's blood parameter based on the obtained set of characteristics of the molecular and cellular structure of blood and information from a pre-compiled database. 2. A wearable device according to claim 1, further comprising a housing, a processor, a battery and a storage device located in the housing. 3. The wearable device according to claim 1 or 2, wherein said at least one blood parameter is a hemoglobin concentration, total hemoglobin mass, hematocrit, total red blood cell volume, blood volume, plasma volume. 4. A wearable device according to any one of paragraphs. 1-3, while determination of blood parameter values is based on comparison of the generated set of blood structure characteristics with sets of blood structure characteristics from the database using a trained predictive model; the database is a database in which sets of blood structure characteristics are compared with specific reference values of at least one blood parameter for a plurality of users, determined by a laboratory method. 5. A wearable device according to claim 4, wherein the predictive model is trained using a compiled database using a machine learning algorithm or a neural network. 6. The wearable device according to claim 4 or 5, wherein the compiled database contains profile data of a plurality of users. 7. A wearable device according to any one of paragraphs. 1-6, wherein the wearable device is configured to, when processing BIA signals and obtaining characteristics of the cellular structure of blood, calculate the total amount of body fluid (TBW) and the ratio of the amount of intracellular fluid (ICW) to the amount of extracellular fluid (ECW). 8. A wearable device according to any one of paragraphs. 1-7, with the different frequencies mentioned being in the ranges of 20-40 kHz and 200-300 kHz, respectively. 9. A wearable device according to any one of paragraphs. 1-8, wherein said at least one PPG sensor comprises at least one radiation source comprising two or more light-emitting diodes, and at least one radiation receiver, preferably a broadband photodetector. 10. A wearable device according to any one of paragraphs. 1-9, with one of the various wavelengths mentioned being in the range 495-570 nm. 11. A wearable device according to any one of paragraphs. 1-10, while converting PPG signals from time domain to frequency domain comprises a Fourier transform or a Hilbert-Huang transform; extracting harmonics from a reference signal involves determining the coordinates of the fundamental harmonic and calculating the coordinates of other harmonics; describing PPG signals with respect to harmonics includes measuring for each harmonic amplitude, curve area, peak width at half height, and/or signal-to-noise ratio to obtain a set of amplitude-phase characteristics of the harmonics. 12. A wearable device according to any one of paragraphs. 1-11, comprising a storage device configured to store a database. 13. A wearable device according to any one of paragraphs. 4-12, wherein the wearable device is configured to correlate a certain blood parameter value using a blood parameter value obtained by a laboratory method, update the database and refine the predictive model using a machine learning algorithm or neural network. 14. A wearable device according to any one of paragraphs. 6-13, additionally containing: an input and output device configured to input user profile data and display a determined blood parameter value, and a communication module configured to exchange information with a remote server and/or cloud storage. 15. A wearable device according to any one of paragraphs. 1-14, wherein the wearable device is configured to be placed on the wrist. 16. A wearable device according to any one of paragraphs. 1-15, wherein the wearable device is a smart device, preferably a smart watch or a fitness bracelet. 17. A method for determining at least one parameter of a user’s blood, comprising the steps of: irradiate the user's tissue with radiation of at least two different wavelengths, recording at least two photoplethysmographic (PPG) signals in the time domain at said at least two different wavelengths in reflection mode, pass alternating current through the user's body, record the corresponding bioimpedance (BIA) electrical signals caused by the passage of alternating current, process the registered PPG and BIA signals to obtain from them characteristics of the molecular and cellular structure of blood and form a set of characteristics of the molecular and cellular structure of blood that determine the state of the blood structure; And determine the value of at least one user’s blood parameter based on the obtained set of characteristics of the molecular and cellular structure of blood and information from a pre-compiled database, at the same time, at the stage of processing PPG signals and obtaining characteristics of the molecular structure of blood, the registered PPG signals are converted from the time domain to the frequency domain, select the most significant signal from all PPG signals, characterized by the highest signal-to-noise ratio, the highest amplitude and/or the least noise, and use it as a reference signal, harmonics are extracted from the reference signal and used as coordinates, all converted PPG signals are laid out along the mentioned coordinates, the value of the fundamental harmonic, the magnitude of the amplitude-phase characteristics of other harmonics of at least two PPG signals with different wavelengths are determined, and the converted PPG signals are described. signals regarding harmonics with obtaining characteristics of the molecular structure of blood; at the same time, at the stage of processing BIA signals and obtaining characteristics of the cellular structure of the blood, the voltage drop across the electrodes is recorded and at least one parameter of the impedance of the user's body is obtained, such as the value of the impedance modulus of the user's body, the phase angle of the impedance of the user's body at different frequencies. 18. The method according to claim 17, wherein said at least one blood parameter is a hemoglobin concentration, total hemoglobin mass, hematocrit, total red blood cell volume, blood volume, plasma volume. 19. The method according to claim 17 or 18, in which at the stage of processing BIA signals and obtaining characteristics of the cellular structure of the blood, the total amount of body fluid (TBW) and the ratio of the amount of intracellular fluid (ICW) to the amount of extracellular fluid (ECW) are calculated. 20. Method according to any one of paragraphs. 17-19, in which said different frequencies are in the ranges of 20-40 kHz and 200-300 kHz, respectively. 21. Method according to any one of paragraphs. 17-20, in which, at the stage of determining the value of blood parameters, the generated set of blood structure characteristics is compared with sets of blood structure characteristics from a pre-compiled database, each of which corresponds to a specific reference value of at least one blood parameter for multiple users, determined by a laboratory method, using a trained predictive model. 22. Method according to any one of paragraphs. 17-21, in which the irradiation is carried out using at least one radiation source, which is two or more light-emitting diodes, and the registration of PPG signals is carried out using at least one radiation receiver, preferably a broadband photodetector. 23. Method according to any one of paragraphs. 17-22, wherein one of the at least two different wavelengths is in the range of 495-570 nm. 24. Method according to any one of paragraphs. 17-23, in which the conversion of PPG signals from time domain to frequency domain is carried out by Fourier transform or Hilbert-Huang transform. 25. Method according to any one of paragraphs. 17-24, in which extracting harmonics from a reference signal involves determining the coordinate of the fundamental harmonic and calculating the coordinates of other harmonics. 26. Method according to any one of paragraphs. 17-25, in which the description of PPG signals with respect to harmonics includes measuring for each harmonic amplitude, curve area, peak width at half height, and/or signal-to-noise ratio to obtain a set of amplitude-phase characteristics of the harmonics. 27. Method according to any one of paragraphs. 21-26, wherein the predictive model is trained on a compiled database using a machine learning algorithm or neural network. 28. Method according to any one of paragraphs. 17-27, wherein the database is configured to be stored on a storage device, a remote server and/or in a cloud storage. 29. Method according to any one of paragraphs. 21-28, further including correlating a determined value of said at least one blood parameter using a laboratory-derived blood parameter value, updating the database, and refining the predictive model using a machine learning algorithm or neural network. 30. A system for determining at least one user’s blood parameter, comprising a remote server and/or cloud storage and a wearable device containing: at least one photoplethysmography (PPG) sensor configured to irradiate the user's tissue with radiation of at least two different wavelengths and detect at least two optical PPG signals at the at least two different wavelengths in a reflection mode, a bioimpedance (BIA) sensor containing two pairs of electrodes and configured to pass alternating current through the user's body and record corresponding electrical signals, wherein the wearable device is configured to: process the signals registered by the mentioned sensors, obtain from them the characteristics of the molecular and cellular structure of the blood and form a set of characteristics of the blood structure that determine the state of the blood structure; when processing PPG signals and obtaining characteristics of the molecular structure of blood, convert the recorded PPG signals from the time domain to the frequency domain, select from all PPG signals the most significant signal, characterized by the highest signal-to-noise ratio, the highest amplitude and/or the least noise, and use it as a reference signal, extract harmonics from the reference signal and use them as coordinates, decompose all converted PPG signals according to the mentioned coordinates, determine the value of the fundamental harmonic and the magnitude of the amplitude-phase characteristics of other harmonics of at least two PPG signals with different wavelengths and describe the converted PPG signals relative to harmonics to obtain characteristics of the molecular structure of blood; when processing BIA signals and obtaining characteristics of the cellular structure of blood, register the voltage drop across the electrodes to obtain at least one parameter of the user's body impedance, such as the value of the user's body impedance modulus, the phase angle of the user's body impedance at different frequencies; And determine the value of at least one user’s blood parameter based on the obtained set of characteristics of the molecular and cellular structure of blood and information from a pre-compiled database, the system contains means for exchanging information between the wearable device and a remote server and/or cloud storage. 31. The system according to claim 30, wherein the wearable device further comprises a housing, a processor, a battery and a storage device located in the housing. 32. The system according to claim 30 or 31, wherein said at least one blood parameter is a hemoglobin concentration, total hemoglobin mass, hematocrit, total red blood cell volume, blood volume, plasma volume. 33. The system according to any one of paragraphs. 30-32, while determining the values of blood parameters is based on comparing the generated set of blood structure characteristics with sets of blood structure characteristics from the database using a trained predictive model; the database is a database in which sets of blood structure characteristics are compared with specific reference values of at least one blood parameter for a plurality of users, determined by a laboratory method. 34. The system according to claim 33, wherein the predictive model is trained using a compiled database using a machine learning algorithm or a neural network. 35. The system of claim 33 or 34, wherein the compiled database contains profile data of a plurality of users. 36. The system according to any one of paragraphs. 30-35, wherein the wearable device is configured to calculate the total amount of body fluid (TBW) and the ratio of the amount of intracellular fluid (ICW) to the amount of extracellular fluid (ECW) when processing BIA signals and extracting characteristics of the cellular structure of blood. 37. The system according to any one of paragraphs. 30-36, with the different frequencies mentioned being in the ranges of 20-40 kHz and 200-300 kHz, respectively. 38. The system according to any one of paragraphs. 30-37, wherein said at least one PPG sensor contains at least one radiation source containing two or more light-emitting diodes, and at least one radiation receiver, preferably a broadband photodetector. 39. The system according to any one of paragraphs. 30-38, with one of the various wavelengths mentioned being in the range 495-570 nm. 40. The system according to any one of paragraphs. 30-39, while converting PPG signals from time domain to frequency domain comprises a Fourier transform or a Hilbert-Huang transform; extracting harmonics from a reference signal involves determining the coordinates of the fundamental harmonic and calculating the coordinates of other harmonics; describing PPG signals with respect to harmonics includes measuring for each harmonic amplitude, curve area, peak width at half height, and/or signal-to-noise ratio to obtain a set of amplitude-phase characteristics of the harmonics. 41. The system according to any one of paragraphs. 30-40, wherein the wearable device includes a storage device configured to store a database. 42. The system according to any one of paragraphs. 33-41, wherein the wearable device is configured to correlate a certain blood parameter value using a blood parameter value obtained by a laboratory method, update the database and refine the predictive model using a machine learning algorithm or neural network. 43. The system according to any one of paragraphs. 35-42, wherein the wearable device additionally contains: an input and output device configured to input user profile data and display a determined blood parameter value, and a communication module configured to exchange information with a remote server and/or cloud storage. 44. The system according to any one of paragraphs. 30-43, wherein the wearable device is designed to be placed on the wrist. 45. The system according to any one of paragraphs. 30-44, wherein the wearable device is a smart device, preferably a smart watch or fitness bracelet. 46. The system according to any one of paragraphs. 30-45, wherein the database is designed for storage on a storage device, a remote server and/or in a cloud storage and is configured to access user profile data from external devices through a remote server and/or cloud storage. 47. The system according to any one of paragraphs. 31-46, wherein the user profile data and the results of previous determinations of the value of the at least one blood parameter are stored on the storage device, remote server and/or cloud storage. 48. The system of claim 47, further comprising means for accessing user profile data and the results of previous determinations of the value of said at least one blood parameter from other electronic devices.