WO2025034809A1 - Blood pressure estimation using near-infrared spectroscopy - Google Patents

Abstract

Provided herein is a computer-implemented method of determining blood pressure in a patient, including steps of transmitting light into a patient's limb distal to an occlusion point, receiving, first data relating to a pressure applied to occlude a patient's limb, receiving second data relating to a parameter of the transmitted light at a first time point during occlusion of the patient's limb, receiving third data relating to the parameter of the transmitted light at a second time point after perfusion of the patient's limb has resumed, comparing the second data to the third data determine a characteristic of the transmitted light, and determining the patient's systolic blood pressure based at least on the first data and the characteristic of the transmitted light.

Description

BLOOD PRESSURE ESTIMATION USING NEAR-INFRARED SPECTROSCOPY

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to United States Provisional Patent Application No. 63/531,491 filed August 8, 2023, and United States Provisional Patent Application No. 63/544,837 filed October 19, 2023, the disclosures of which are hereby incorporated by reference in their entireties.

BACKGROUND OF THE INVENTION

Field of the Invention

[0002] The present invention is directed to systems and methods for determining blood pressure, and, in particular embodiments, systems and methods for determining blood pressure through alterations in light intensity.

Description of Related Art

[0003] Hypertension is faulted with more than 270k cardiovascular disease-related deaths per year, and this figure continues to grow year after year. The ability of blood pressure measurements to predict cardiovascular disease has prompted substantial research aimed at frequent, non-invasive methods of determining blood pressure.

[0004] The current, standard method for non-invasive blood pressure measurement remains an oscillometric technique in which one of the subjects’ arms, being supported level with the heart, is fit with a pressure cuff situated around the brachial artery in the upper arm and inflated to suprasystolic pressure, occluding blood flow to the arm, then slowly deflated to subdiastolic pressure. During the deflation process, distal to the occluding cuff, a pulsatile pressure signal appears approximately at systolic pressure as the internal pressure of the brachial artery begins to exceed that of the cuff. This signal achieves maximum amplitude at mean arterial pressure (MAP) and decreases in amplitude until the cuff pressure reaches approximately diastolic pressure. The magnitude envelope of this pulsatile signal is referred to as the oscillogram. Several factors affect the accuracy of this technique, including the subjects’ clothing, background noise, and the skill of the examiner. Because this technique requires a trained professional, it is difficult to implement for home monitoring and is also prone to bias, for example from the anxiety subjects experience in clinical settings.

[0005] Several automated methods exist as an alternative to the auscultatory approach. The most common of these are oscillometric methods which utilize the pressure oscillogram extracted during deflation of the pressure cuff. However, variation between subjects, particular disease conditions, and age groups impart an error of 5-10 mmHg onto this standard for blood pressure determination — or approximately 5% of the value being measured. Several cuffless wearable approaches have emerged in recent years that permit more frequent blood pressure estimates, but the accuracy of these techniques falls short of oscillometric approaches. Accordingly, there is a need in the field for systems that may allow for improved, accurate determination of blood pressure.

SUMMARY OF THE INVENTION

[0006] Provided herein is a computer-implemented method of determining blood pressure in a patient, including steps of transmitting light into a patient’s limb distal to an occlusion point, receiving, with at least one processor and from at least one first sensor, first data relating to a pressure applied to occlude a patient’s limb, receiving, with at least one processor and from at least one second sensor, second data relating to a parameter of the transmitted light at a first time point during occlusion of the patient’s limb, receiving, with at least one processor and from the at least one second sensor, third data relating to the parameter of the transmitted light at a second time point after perfusion of the patient’ s limb has resumed, comparing, with at least one processor, the second data to the third data determine a characteristic of the transmitted light, and determining, with at least one processor, the patient’s systolic blood pressure based at least on the first data and the characteristic of the transmitted light.

[0007] Also provided herein is a a method of non-invasively determining a patient’s blood pressure, including steps of applying pressure to a patient’s limb at an occlusion point, thereby occluding one or more blood vessels within the limb, arranging a near-infrared light source and a sensor on the patient’s limb distal to the occlusion point, transmitting, with the light source, light into the patient’ s limb distal to the occlusion point, receiving, with at least one processor and from at least one first sensor, first data relating to a pressure applied to occlude a patient’s limb, receiving, with at least one processor and from at least one second sensor, second data relating to a parameter of the transmitted light at a first time point during occlusion of the one or more blood vessels within the patient’s limb, reducing the pressure applied to the patient’s limb, thereby allowing perfusion of the patient’s limb distal to the occlusion point, receiving, with at least one processor and from the at least one second sensor, third data relating to the parameter of the transmitted light at a second time point after perfusion of the patient’s limb has resumed, comparing, with at least one processor, the second data and the third data to determine a characteristic of the transmitted light, and determining, with at least one processor, the patient’s systolic blood pressure based at least on the first data and the characteristic of the transmitted light..

[0008] Also provided herein is a system for determining a patient’s blood pressure, including at least one processor configured to receive, from at least one first sensor, first data relating to a pressure applied to occlude a patient’s limb, receive, from at least one second sensor, second data relating a parameter of light transmitted into the patient’s limb at a first time point during occlusion of the patient’s limb, receive, from the at least one second sensor, third data relating to the parameter of light transmitted into the patient’s limb at a second time point after perfusion of the patient’s limb has resumed, compare the second data to the third data to determine a characteristic of the transmitted light, and determine the patient’s systolic blood pressure based at least on the first data and the characteristic of the transmitted light. [0009] Also provided herein is system for determining a patient’s blood pressure, including a pressure cuff, at least one pressure sensor, a near-infrared light source, at least one light sensor configured to detect light having a wavelength of from about 760 nm to about 850 nm, at least one processor configured to cause the pressure cuff to apply pressure to a limb of the patient at an occlusion point, thereby occluding blood flow distal to the occlusion point, receive, from the at least one pressure sensor, first data relating to the pressure applied to occlude a patient’s limb, receive, from the at least one light sensor, second data relating a parameter of light transmitted into the patient’ s limb at a first time point during occlusion of the patient’s limb, cause the pressure cuff to deflate, thereby releasing pressure on the limb of the patient and allowing blood flow through the occlusion point, receive, from the at least one light sensor, third data relating to the parameter of light transmitted into the patient’ s limb at a second time point after blood flow has resumed through the occlusion point, compare the second data to the third data to determine a characteristic of the transmitted light, and determine the patient’s systolic blood pressure based at least on the first data and the characteristic of the transmitted light.

[0010] Further non-limiting embodiments are set forth in the following numbered clauses:

[0011] Clause 1: A computer- implemented method of determining blood pressure in a patient, comprising: transmitting light into a patient’s limb distal to an occlusion point; receiving, with at least one processor and from at least one first sensor, first data relating to a pressure applied to occlude a patient’s limb; receiving, with at least one processor and from at least one second sensor, second data relating to a parameter of the transmitted light at a first time point during occlusion of the patient’s limb; receiving, with at least one processor and from the at least one second sensor, third data relating to the parameter of the transmitted light at a second time point after perfusion of the patient’ s limb has resumed; comparing, with at least one processor, the second data to the third data determine a characteristic of the transmitted light; and determining, with at least one processor, the patient’s systolic blood pressure based at least on the first data and the characteristic of the transmitted light.

[0012] Clause 2: The method of clause 1, wherein the characteristic is an inflection point in the transmitted light.

[0013] Clause 3: The method of clause 1 or clause 2, wherein the parameter of the transmitted light corresponds to a change in hemoglobin concentration and/or hemoglobin saturation, optionally to an amount of oxygenated hemoglobin.

[0014] Clause 4: The method of any of clauses 1-3, further comprising determining, with at least one processor and with oscillometry, the patient’s blood pressure.

[0015] Clause 5: The method of any of clauses 1-4, further comprising: receiving, with at least one processor and from the at least one second sensor, data relating to a measure of pulsatility of the patient’ s heart; and determining, with at least one processor and based at least on the measure of pulsatility, the patient’s diastolic blood pressure.

[0016] Clause 6: The method of any of clauses 1-5, further comprising filtering, with at least one processor, the data relating to the parameter of the transmitted light.

[0017] Clause 7: The method of any of clauses 1-6, wherein the light source is a nearinfrared light source.

[0018] Clause 8: The method of any of clauses 1-7, wherein the transmitted light has a wavelength of about 600 nm to about 800 nm, optionally about 760 nm.

[0019] Clause 9: The method of any of clauses 1-8, wherein the transmitted light has a wavelength of about 800 nm to about 2500 nm, optionally about 850 nm.

[0020] Clause 10: The method of any of clauses 1-9, wherein at the least one sensor is configured to detect light having a wavelength of about 760 nm to about 850 nm.

[0021] Clause 11: A method of non-invasively determining a patient’ s blood pressure, comprising: applying pressure to a patient’s limb at an occlusion point, thereby occluding one or more blood vessels within the limb; arranging a near- infrared light source and a sensor on the patient’ s limb distal to the occlusion point; transmitting, with the light source, light into the patient’s limb distal to the occlusion point; receiving, with at least one processor and from at least one first sensor, first data relating to a pressure applied to occlude a patient’s limb; receiving, with at least one processor and from at least one second sensor, second data relating to a parameter of the transmitted light at a first time point during occlusion of the one or more blood vessels within the patient’s limb; reducing the pressure applied to the patient’s limb, thereby allowing perfusion of the patient’s limb distal to the occlusion point; receiving, with at least one processor and from the at least one second sensor, third data relating to the parameter of the transmitted light at a second time point after perfusion of the patient’ s limb has resumed; comparing, with at least one processor, the second data and the third data to determine a characteristic of the transmitted light; and determining, with at least one processor, the patient’s systolic blood pressure based at least on the first data and the characteristic of the transmitted light.

[0022] Clause 12: The method of clause 11, wherein the characteristic is an inflection point in the transmitted light.

[0023] Clause 13: The method of clause 11 or clause 12, wherein the parameter of the transmitted light corresponds to an amount of oxygenated hemoglobin.

[0024] Clause 14: The method of any of clauses 11-13, further comprising determining, with at least one processor and with oscillometry, the patient’s blood pressure.

[0025] Clause 15: The method of any of clauses 11-14, further comprising: receiving, with at least one processor and from the at least one second sensor, data relating to a measure of pulsatility of the patient’s heart; and determining, with at least one processor and based at least on the measure of pulsatility, the patient’s diastolic blood pressure.

[0026] Clause 16: The method of any of clauses 11-15, further comprising filtering, with at least one processor, the data relating to the parameter of the transmitted light.

[0027] Clause 17: The method of any of clauses 11-16, wherein the transmitted light has a wavelength of about 600 nm to about 800 nm, optionally about 760 nm.

[0028] Clause 18: The method of any of clauses 11-17, wherein the transmitted light has a wavelength of about 800 nm to about 2500 nm, optionally 850 nm.

[0029] Clause 19: The method of any of clauses 11-18, wherein the sensor is configured to detect light having a wavelength of about 760 nm to about 850 nm.

[0030] Clause 20: A system for determining a patient’s blood pressure, comprising at least one processor configured to: receive, from at least one first sensor, first data relating to a pressure applied to occlude a patient’s limb; receive, from at least one second sensor, second data relating a parameter of light transmitted into the patient’ s limb at a first time point during occlusion of the patient’s limb; receive, from the at least one second sensor, third data relating to the parameter of light transmitted into the patient’ s limb at a second time point after perfusion of the patient’s limb has resumed; compare the second data to the third data to determine a characteristic of the transmitted light; and determine the patient’ s systolic blood pressure based at least on the first data and the characteristic of the transmitted light.

[0031] Clause 21 : The system of clause 20, wherein the at least one processor is further configured to: receive, from the at least one second sensor, fourth data relating the parameter of light transmitted into the patient’s limb at third time point after perfusion of the patient’s limb has resumed; and determine, based at least the fourth data, a measure of pulsatility; and determine, based at least one the measure of pulsatility, the patient’s diastolic blood pressure. [0032] Clause 22: The system of clause 20 or clause 21, wherein the at least one processor is further configured to: filter the data relating to the parameter of the transmitted light.

[0033] Clause 23: The system of any of clauses 20-22, wherein the light that is transmitted into the patient’ s limb is near-infrared light.

[0034] Clause 24: The system of any of clauses 20-23, wherein the transmitted light has a wavelength of about 600 nm to about 800 nm, optionally about 760 nm.

[0035] Clause 25: The system of any of clauses 20-24, wherein the transmitted light has a wavelength of about 800 nm to about 2500 nm, optionally about 850 nm.

[0036] Clause 26: The system of any of clauses 20-25, wherein at the least one sensor is configured to detect light having a wavelength of about 760 nm to about 850 nm.

[0037] Clause 27: A system for determining a patient’s blood pressure, comprising: a pressure cuff; at least one pressure sensor; a near-infrared light source; at least one light sensor configured to detect light having a wavelength of from about 760 nm to about 850 nm; and at least one processor configured to: cause the pressure cuff to apply pressure to a limb of the patient at an occlusion point, thereby occluding blood flow distal to the occlusion point; receive, from the at least one pressure sensor, first data relating to the pressure applied to occlude a patient’s limb; receive, from the at least one light sensor, second data relating a parameter of light transmitted into the patient’ s limb at a first time point during occlusion of the patient’s limb; cause the pressure cuff to deflate, thereby releasing pressure on the limb of the patient and allowing blood flow through the occlusion point; receive, from the at least one light sensor, third data relating to the parameter of light transmitted into the patient’ s limb at a second time point after blood flow has resumed through the occlusion point; compare the second data to the third data to determine a characteristic of the transmitted light; and determine the patient’s systolic blood pressure based at least on the first data and the characteristic of the transmitted light. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] FIG. 1 shows a setup useful in non-limiting embodiments of methods and systems as described herein. FIG. 1A shows a near-infrared spectroscopy (NIRS) probe positioned distal to an occluding arm cuff which is inflated to a pressure above diastolic pressure and slowly deflated. FIG. IB shows the foregoing performed for several heights of the arm relative to the heart to alter the compliance of the measured vessels. FIG. 1C shows arteries distend (top to bottom) when lower than the heart due to hydrostatic pressure, decreasing their compliance;

[0039] FIG. 2 shows cuff pressure and AHbO data for a single round of occlusion according to non-limiting embodiments described herein;

[0040] FIGS. 3A-3B show plots showing the predicted vs. actual systolic blood pressure (Ps) values, using AHbO, the inflection method (FIG. 3A) was more accurate than the oscillometric method (FIG. 3B);

[0041] FIGS. 4A-4B show inflection (FIG. 4A) and oscillometric (FIG. 4B) methods for determining blood pressure using blood flow;

[0042] FIG. 5 shows oscillometric waveforms (OWM) and envelopes which may be used to estimate systolic pressure, diastolic pressure, and MAP;

[0043] FIG. 6 shows validation of oscillometric dataset produced a RMSE of 11.4 mmHg; and

[0044] FIG. 7 is a schematic diagram of example components of one or more devices useful in non-limiting embodiments of systems and methods according to non-limiting embodiments described herein.

DESCRIPTION OF THE INVENTION

[0045] The use of numerical values in the various ranges specified in this application, unless expressly indicated otherwise, are stated as approximations as though the minimum and maximum values within the stated ranges are both preceded by the word “about”. In this manner, slight variations above and below the stated ranges can be used to achieve substantially the same results as values within the ranges. Also, unless indicated otherwise, the disclosure of ranges is intended as a continuous range including every value between the minimum and maximum values.

[0046] For purposes of the description hereinafter, the terms “end,” “upper,” “lower,” “right,” “left,” “vertical,” “horizontal,” “top,” “bottom,” “lateral,” “longitudinal,” and derivatives thereof shall relate to the embodiments as they are oriented in the drawing figures. However, it is to be understood that the present disclosure may assume various alternative variations and step sequences, except where expressly specified to the contrary. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply exemplary and non-limiting embodiments or aspects of the disclosed subject matter. Hence, specific dimensions and other physical characteristics related to the embodiments or aspects disclosed herein are not to be considered as limiting.

[0047] As used herein, the term “comprising” is open-ended and may be synonymous with ‘including’ , ‘containing’ , or ‘characterized by ’ . The term "consisting essentially of" limits the scope of a claim to the specified materials or steps, and those that do not materially affect basic and novel characteristic(s). The term “consisting of" excludes any element, step, or ingredient not specified in the claim. As used herein, embodiments "comprising" one or more stated elements or steps also include but are not limited to embodiments "consisting essentially of" and "consisting of" these stated elements or steps. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms.

[0048] As used herein, the term “patient” or “subject” refers to members of the animal kingdom including but not limited to human beings, and “mammal” refers to all mammals, including, but not limited to human beings.

[0049] No aspect, component, element, structure, act, step, function, instruction, and/or the like used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items and may be used interchangeably with “one or more” and “at least one.” Furthermore, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, and/or the like) and may be used interchangeably with “one or more” or “at least one.” Where only one item is intended, the term “one” or similar language is used. Further, the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise. In addition, reference to an action being “based on” a condition may refer to the action being “in response to” the condition. For example, the phrases “based on” and “in response to” may, in some non-limiting embodiments or aspects, refer to a condition for automatically triggering an action (e.g., a specific operation of an electronic device, such as a computing device, a processor, and/or the like).

[0050] As used herein, the term “communication” may refer to the reception, receipt, transmission, transfer, provision, and/or the like of data (e.g., information, signals, messages, instructions, commands, and/or the like). For one unit (e.g., a device, a system, a component of a device or system, combinations thereof, and/or the like) to be in communication with another unit means that the one unit is able to directly or indirectly receive information from and/or transmit information to the other unit. This may refer to a direct or indirect connection (e.g., a direct communication connection, an indirect communication connection, and/or the like) that is wired and/or wireless in nature. Additionally, two units may be in communication with each other even though the information transmitted may be modified, processed, relayed, and/or routed between the first and second unit. For example, a first unit may be in communication with a second unit even though the first unit passively receives information and does not actively transmit information to the second unit. As another example, a first unit may be in communication with a second unit if at least one intermediary unit processes information received from the first unit and communicates the processed information to the second unit. In some non-limiting embodiments or aspects, a message may refer to a network packet (e.g., a data packet and/or the like) that includes data. It will be appreciated that numerous other arrangements are possible. Communication may include one or more wired and/or wireless networks. For example, communication may include a cellular network (e.g., a long-term evolution (LTE) network, a third-generation (3G) network, a fourth-generation (4G) network, a fifth-generation (5G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the public switched telephone network (PSTN) and/or the like), a private network, an ad hoc network, an intranet, the Internet, a fiber optic -based network, a cloud computing network, and/or the like, and/or a combination of some or all of these or other types of networks.

[0051] As used herein, the term “computing device” may refer to one or more electronic devices configured to process data. A computing device may, in some examples, include the necessary components to receive, process, and output data, such as a processor, a display, a memory, an input device, a network interface, and/or the like. A computing device may be a mobile device. As an example, a mobile device may include a cellular phone (e.g., a smartphone or standard cellular phone), a portable computer, a wearable device (e.g., watches, glasses, lenses, clothing, and/or the like), a personal digital assistant (PDA), and/or other like devices. A computing device may also be a desktop computer or other form of non-mobile computer.

[0052] As used herein, the term “server” may refer to or include one or more computing devices that are operated by or facilitate communication and processing for multiple parties in a network environment, such as the Internet, although it will be appreciated that communication may be facilitated over one or more public or private network environments and that various other arrangements are possible. Further, multiple computing devices (e.g., servers, mobile devices, etc.) directly or indirectly communicating in the network environment may constitute a “system.”

[0053] As used herein, the term “system” may refer to one or more computing devices or combinations of computing devices (e.g., processors, servers, client devices, software applications, components of such, and/or the like). Reference to “a device,” “a server,” “a processor,” and/or the like, as used herein, may refer to a previously-recited device, server, or processor that is recited as performing a previous step or function, a different device, server, or processor, and/or a combination of devices, servers, and/or processors. For example, as used in the specification and the claims, a first device, a first server, or a first processor that is recited as performing a first step or a first function may refer to the same or different device, server, or processor recited as performing a second step or a second function.

[0054] Provided herein are systems and methods for determining one or more physiological parameters of a patient, such as a patient’s blood pressure. The systems and methods disclosed herein represent a technological improvement over known methods, by providing improved accuracy and less invasiveness, due, at least in part, to the use of parameters of light, such as light intensity, which are based on physiologic parameters such as blood oxygenation, blood flow, blood volume, and like parameters. Such parameters do not require a healthcare professional to, for example, subjectively identify a measure of pulsatility during the process. Rather, the systems and methods disclosed herein make use of objective parameters and provide a higher degree of accuracy, allowing for medical professionals to respond and perform interventions in a shorter period of time. In addition, the systems and methods disclosed herein do not require a measure pulsatility (though such measures may be used), which broadens the patient population for which highly accurate measurements of physiological parameters, such as blood pressure or mean arterial pressure (MAP), may be obtained.

[0055] Accordingly, disclosed herein are non-invasive methods, including computer- implemented methods, of determining a vascular parameter, such as blood pressure, in a patient. In non-limiting embodiments, the methods include obtaining data before, during, and/or following cessation of occlusion of a limb of a patient. In non-limiting embodiments, the limb may be an arm, a wrist, a finger, a leg, an ankle, and/or a toe. In non-limiting embodiments the limb is occluded such that blood flow through one or more vessels is, at least temporarily, ceased. In non-limiting embodiments, the limb is occluded using a known technique, such as a pressure cuff as is known in the art and which is typically used in combination with oscillometric methods and/or auscultatory methods.

[0056] Data obtained prior to, during, and/or following cessation of occlusion may include data relating to pressure being applied to the limb and data relating to one or more physiological parameters. Data relating to one or more physiological parameters may be obtained through one or more sensors, including pressure sensors, temperature sensors, galvanic skin response sensors, and/or light sensors. In non-limiting embodiments, methods disclosed herein include transmitting light into the limb that is being occluded, for example distal to the point of occlusion, for example by affixing a light-emitting device, such as a diode, for example a laser diode, on the patient proximal and/or distal to the point of occlusion. In non-limiting embodiments, any wavelength of light may be used. In non-limiting embodiments, a laser diode is affixed to the patient distal to the point of occlusion.

[0057] In non-limiting embodiments, the light that is transmitted into the limb may be near-infrared light, and one or more sensors may be arranged to measure absorption of the light by one or more compounds within the patient’s tissue. In such non-limiting embodiments, near-infrared spectroscopy (NIRS) may be utilized. As used herein, “near-infrared light” means electromagnetic radiation having a wavelength of from greater than about 700 nm to about 2500 nm. In non-limiting embodiments, the light that is utilized has a wavelength of about 700 nm to about 2500 nm, in non-limiting embodiments about 760 nm to about 2500 nm, in non-limiting embodiments about 780 nm to about 2500 nm, in non-limiting embodiments about 850 nm to about 2500 nm, in non-limiting embodiments about 760 nm, in non-limiting embodiments about 850 nm, in non-limiting embodiments up to about 2500 nm, and/or in nonlimiting embodiments between about 760 nm and about 850 nm, all values and subranges therebetween inclusive. In non-limiting embodiments, the light that is utilized has a wavelength of about 760 nm and/or about 850 nm.

[0058] Devices and systems for emitting light (for example, to transmit light into a patient’s tissue, such as a limb) and for sensing light, for example devices and systems for NIRS, are commercially available from, for example, NIRx, and Artinis Medical Systems. In non-limiting embodiments, a pulse oximeter may be used in the methods and systems described herein. In non-limiting embodiments, single-channel sensors may be utilized. In non-limiting embodiments, multi-channel sensors may be utilized.

[0059] As noted above, in non-limiting embodiments, NIRS may be utilized, distal to the point of limb occlusion, to detect one or more physiological parameters, including, for example and without limitation, blood volume, blood flow, and/or blood oxygenation. In nonlimiting embodiments, blood oxygenation may be determined with NIRS based on levels of oxygenated hemoglobin, in non-limiting embodiments based on a ratio of oxygenated hemoglobin to non-oxygenated hemoglobin. In non-limiting embodiments, one or more sensors may be utilized to detect a change in one or more parameters of light distal to the point of limb occlusion between a time prior to occlusion of the limb, a time during occlusion of the limb and/or a time after occlusion of the limb has been discontinued (e.g., such that reperfusion of the limb distal to the occlusion has begun), and these changes may be correlated, for example, with a device or system as described herein, to the pressure being applied to the limb at the time of the change(s). In non-limiting embodiments, the pressure that is applied to the limb at the time of the detected change is the patient’s systolic blood pressure.

[0060] For example, and without limitation as shown schematically in FIG. 1A, a change in oxygenated hemoglobin (AHbO) distal to the point of limb occlusion may be detected with NIRS. As shown in FIG. 1A, a NIRS probe may be positioned distal to an occluding arm cuff which is inflated to a pressure above diastolic pressure and slowly deflated. The AHbO is expected to decrease during occlusion and form an inflection point when cuff pressure equals arterial pressure.

[0061] Without wishing to be bound by the theory, it is believed that AHbO decreases during occlusion of the limb, as tissue distal to the occlusion point consumes oxygen, and that this decrease is detectable with, for example NIRS, through the difference in absorption of the near-infrared light between oxygenated hemoglobin and deoxygenated hemoglobin. In nonlimiting embodiments, the decrease in AHbO forms an inflection point during deflation of the cuff, when oxygenated blood begins to reperfuse tissue distal to the occlusion point. This reperfusion occurs at the point where cuff pressure equals arterial pressure, and thus the cuff pressure that is detected when the AHbO reaches an inflection point is equivalent to the patient’s systolic blood pressure. While blood oxygenation, and in embodiments oxygenated hemoglobin, are exemplified above and in the following Examples, those of skill in the art will appreciate that other parameters of light, such as light intensity and/or phase, may be utilized, and that other physiological parameters may be determined with the light-based transmission and sensing methods described herein, including hemoglobin concentration, hemoglobin saturation, blood flow, blood volume, vascular resistance, vascular compliance, water content, and the like.

[0062] In non-limiting embodiments, methods disclosed herein may include continuing to monitor one or more parameters of light during cuff deflation to allow for further blood flow to return to the limb distal to the occlusion point. In non-limiting embodiments, the patient’s diastolic blood pressure may be determined at this time, for example using light transmission and sensors described herein, to determine pulsatility, which may be converted to a diastolic blood pressure by passing the pulsatile optical data into an oscillometric algorithm.

[0063] In non-limiting embodiments, methods as described herein may be combined with oscillometric methods to determine a patient’s blood pressure. For example, a device as described herein, being utilized to determine blood pressure based on parameters of light as described herein, may analyze light data and/or oscillometric data and determine blood pressure based on data obtained using both methods. In non-limiting embodiments, the light data is oscillometric data. In non-limiting embodiments, one or more oscillometric methods are applied to light data and/or pressure data, and may be combined with data generated from methods and systems as disclosed herein. In non-limiting embodiments, pressure determined based on parameters of light as described herein may be used in combination with oscillometric data as described herein to provide a check for errors, for example if a pressure value determined by one method is inconsistent (e.g., 1, 2, 3, 4, or more standard deviations) with a pressure value determined by the other method. In non-limiting embodiments, pressure values determined by the methods may be combined in a linear or non-linear fashion to produce a final pressure value. In non-limiting embodiments, pressure values determined by both methods may be combined, for example as referenced above, for example in a weighted average, to produce a final pressure value. Oscillometric methods are known to those of skill in the art, and generally include alternating the external pressure of an artery between suprasystolic and subdiastolic pressure levels. The external pressure may then be measured and high-pass filtered to yield oscillations indicative of the blood pressure. Peak-to-peak amplitude of oscillations in the measured external pressure vary as a function of the transmural pressure. Blood pressure may then be estimated from the oscillation amplitude versus external pressure function via an algorithm. Such algorithms may include the maximum amplitude algorithm, the fixed ratio algorithm, and the derivative algorithm, and those of skill will appreciate that any known oscillometric algorithm may be utilized to obtain a pressure value using oscillometry, and that such pressure values may be combined with those determined using the methods disclosed herein to provide a patient’s blood pressure.

[0064] In non-limiting embodiments, data, for example light data, for example absorbance data, sensed by one or more sensors may be processed, for example, filtered, prior to being used to determine a physiological parameter such as blood pressure. Software and algorithms for processing and/or filtering data, for example NIRS data, are known to those of skill in the art and may include bandpass filtering, band-stop filtering, recursive filtering, motion artifact removal, component analysis, wavelength analysis, and/or machine learning. [0065] In non-limiting embodiments, a patient’s vascular compliance, for example arterial compliance, may be determined with the methods disclosed herein. For example, as shown in FIG. IB, an inflection point in a parameter of light, for example an inflection point in HbO as described herein, may be determined at a variety of different arm angles and heights. As shown in FIG. 1C, arteries distend (from the top of the figure to the bottom) when lower than the heart due to hydrostatic pressure, decreasing their compliance. A difference between the maximum inflection point and the minimum inflection point may then be determined, and that difference is equivalent to the patient’s compliance.

[0066] In non-limiting embodiments, a patient’s MAP may be determined with the methods disclosed herein. For example, in patients with various cardiac conditions, for example those experiencing heart failure and/or those using a left ventricular assist device (LVAD), the cuff pressure when reperfusion begins and an inflection point in HbO is reached may correspond to the patient’s MAP.

[0067] Also disclosed herein are devices and systems for implementing the methods described herein above and below. With reference to FIG. 7, shown is an exemplary schematic of a device that may be useful in the presently-disclosed methods. Device 200 may correspond to any element of any system or device described herein, including any computing device and/or server, for example those configured to collect data (e.g., light data, oscillometric data, and/or the like), process/filter such data, and determine a physiological parameter, such as blood pressure, as described herein. In some non-limiting embodiments, such systems or devices may include at least one device 200 and/or at least one component of device 200. The number and arrangement of components shown are provided as an example. In some nonlimiting embodiments, device 200 may include additional components, fewer components, different components, or differently arranged components than those shown. Additionally, or alternatively, a set of components (e.g., one or more components) of device 200 may perform one or more functions described as being performed by another set of components of device 200.

[0068] As shown in FIG. 7, device 200 may include a bus 202, a processor 204, memory 206, a storage component 208, an input component 210, an output component 212, and a communication interface 214. Bus 202 may include a component that permits communication among the components of device 200. In some non-limiting embodiments, processor 204 may be implemented in hardware, firmware, or a combination of hardware and software. For example, processor 204 may include a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and/or any processing component (e.g., a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc.) that can be programmed to perform a function. Memory 206 may include random access memory (RAM), read only memory (ROM), and/or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, optical memory, etc.) that stores information and/or instructions for use by processor 204. It is to be understood that programming instructions, stored in memory (e.g., RAM and/or ROM), which may be executed by a processor (e.g., processor 204) to receive data, analyze data, train a machine learning model, validate a machine learning model, and/or apply a machine learning model, are within the scope of the present disclosure.

[0069] With continued reference to FIG. 7, storage component 208 may store information and/or software related to the operation and use of device 200. For example, storage component 208 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, a solid-state disk, etc.) and/or another type of computer-readable medium. Input component 210 may include a component that permits device 200 to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, etc.). Additionally, or alternatively, input component 210 may include a sensor for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, an actuator, etc.). Sensors useful here may include biochemical sensors, electrochemical sensors, sensors for detecting autonomic tone, sensors for detecting sympathetic tone, and/or the like. Output component 212 may include a component that provides output information from device 200 (e.g., a display, a speaker, one or more lightemitting diodes (LEDs), etc.). Communication interface 214 may include a transceiver-like component (e.g., a transceiver, a separate receiver and transmitter, etc.) that enables device 200 to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. Communication interface 214 may permit device 200 to receive information from another device and/or provide information to another device. For example, communication interface 214 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi® interface, a cellular network interface, and/or the like. [0070] Device 200 may perform one or more processes described herein. Device 200 may perform these processes based on processor 204 executing software instructions stored by a computer-readable medium, such as memory 206 and/or storage component 208. A computer-readable medium may include any non-transitory memory device. A memory device includes memory space located inside of a single physical storage device or memory space spread across multiple physical storage devices. Software instructions may be read into memory 206 and/or storage component 208 from another computer-readable medium or from another device via communication interface 214. When executed, software instructions stored in memory 206 and/or storage component 208 may cause processor 204 to perform one or more processes described herein. Additionally, or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments described herein are not limited to any specific combination of hardware circuitry and software. The term “configured to,” as used herein, may refer to an arrangement of software, device(s), and/or hardware for performing and/or enabling one or more functions (e.g., actions, processes, steps of a process, and/or the like). For example, “a processor configured to” may refer to a processor that executes software instructions (e.g., program code) that cause the processor to perform one or more functions.

[0071] Accordingly, also included within the scope of the present disclosure are devices, such as computing devices, and systems that may receive data relating to one or more parameters of light as described herein (such as, for example, NIRS data), may receive data relating to pressure being applied to occlude a patient’s limb (such as, for example, data from a pressure cuff, whether analog or from a digital pressure transducer), may receive oscillometric data relating to pressure applied to occlude a patient’s limb, may process and/or filter the foregoing data, and/or may correlate pressure data to the data relating to the one or more parameters of light, with or without the oscillometric data, to determine a patient’s blood pressure (for example, a patient’s systolic blood pressure). In non-limiting embodiments, devices and systems as described herein may also control occlusion of the patient’s limb (e.g., through an inflatable cuff and a pump), transmission of light into tissue distal to the occlusion point, and detection of light distal to the occlusion point before, during, and/or after occlusion of the limb has been discontinued.

[0072] Such devices and systems as described herein may be local to the patient (e.g., a computing device an examination room or other location within a medical facility in which a patient is located), or may be remote from the patient (e.g., a computing device that is in wired or wireless communication with one or more components, such as a blood pressure cuff, a light source, and a light sensor, attached to the patient). Such devices and systems allow for determination of a patient’s blood pressure with a high level of accuracy and a low level of invasiveness while the patient is in a medical facility or at home, for example. In non-limiting embodiments, devices and systems as described herein may deliver an alert and/or warning concerning a physiological parameter, for example an excessively high and/or low blood pressure. Such alerts and/or warnings may be audible, tactile, and/or visual, and may be delivered to a computing device associated with a patient, a patient’s healthcare provider, and/or a patient’ s family member.

Example 1

[0073] The accuracy of the techniques disclosed herein may be compared to standard oscillometric methods, applied to both pressure and optical OWMs.

[0074] Materials and. Methods

[0075] Nine subjects were recruited and 3-6 iterations of arm occlusion via a manually inflated blood pressure cuff were performed. The arm was held at a 90° angle beside the body and subjects remained seated throughout the experiment. The cuff was inflated to a maximum pressure of 250 mmHg over 5-30 seconds, which provided enough margin to achieve an approximately steady rate of deflation by the time cuff pressure reached a relevant value (-160 mmHg). Total occlusion time was limited to a maximum of three minutes, with 2-3 minutes of rest between rounds.

[0076] Cuff pressure was monitored with an analog pressure gauge, as well as digitized via a pressure transducer (BPS-BPA, Vernier). The voltage output of this transducer was sampled at 200 Hz by the internal ADC of a microcontroller (Teensy 4.1). AHbO was measured using a commercial NIRS device (Portalite MK1, Artinis) using a single channel with a source detector distance of 2.9 cm and wavelengths of 760 and 850 nm. The NIRS probe was positioned distal to the pressure cuff and adjacent to the brachial artery. In three of the nine subjects, a custom diffuse correlation spectroscopy (DCS) system simultaneously recorded blood flow. All systems were synchronized using an external analog trigger.

[0077] Results and Discussion

[0078] Cuff pressure and AHbO data for a single round of occlusion are depicted in FIG. 2, which shows a time plot detailing cuff pressure (solid gray) and AHbO (dotted black) over the course of a single round of occlusion. The time window of cuff pressure containing the OWM, beginning at about 400 seconds, is bolded for emphasis. The inflection point in AHbO was determined by finding the minimum of AHbO in the time window between the peaks in cuff pressure and AHbO. The accuracy of the resulting systolic pressure was compared to values calculated using a simple oscillometric approach on cuff pressure data. Briefly, this approach bandpassed data from 0.5-10 Hz, determined heart rate via the FFT of the resulting signal, then bandpassed a second time using a narrower filter with cutoffs at +\- 0.1 Hz of the heart rate. This produced the OWM, whose envelope was calculated via the root mean square of a sliding window with a width of 10 sec. The maximum and minimum slope to the left and right of the peak of the envelope were deemed the time points for systolic and diastolic pressure, respectively. This calculation was validated on a dataset of 350 OWMs with associated blood pressures and produced a root-mean squared error (RMSE) of 10.5 mmHg. Optical oscillometry was performed identically but using AHbO data within the same time window. Blood flow from DCS was also analyzed using both the inflection and oscillometric methods.

[0079] FIGS. 3A-4B are plots showing the predicted vs. actual systolic blood pressure (PS) values. Using AHbO, the inflection method (FIG. 3A) was more accurate (RMSE = 4.08 mmHg) than the oscillometric method (FIG. 3B) with no loss of precision. In some embodiments, reperfusion begins when cuff pressure equals systolic pressure. As reperfusion begins when cuff pressure equals systolic pressure, the inflection method may produce more accurate measurements.

[0080] Using blood flow (BF), the inflection (FIG. 4A) and oscillometric (FIG. 4B) methods performed equally well, but were overall less accurate than using AHbO. The underestimation of the oscillometric calculations demonstrates the problems that arise when defining systolic blood pressure at an arbitrary time point in the OWM.

Example 2

[0081] Hypertension is faulted with more than 270k cardiovascular disease-related deaths in the United States in 2018, and this figure continues to growing by 0.5% per year. The ability of blood pressure measurements to predict cardiovascular disease has prompted substantial research aimed at frequent, non-invasive methods of determining blood pressure. The current, standard method for non-invasive blood pressure measurement remains an oscillometric technique in which one of the subjects’ arms, being supported level with the heart, is fit with a pressure cuff situated around the brachial artery in the upper arm and inflated to suprasystolic pressure, occluding blood flow to the arm, then slowly deflated to subdiastolic pressure. During the deflation process, distal to the occluding cuff, a pulsatile pressure signal appears approximately at systolic pressure as the internal pressure of the brachial artery begins to exceed that of the cuff. This signal achieves maximum amplitude at mean arterial pressure (MAP) and decreases in amplitude until the cuff pressure reaches approximately diastolic pressure. The magnitude envelope of this pulsatile signal is referred to as the oscillogram. During this procedure, blood pressure can be determined using a manual, auscultatory approach, or an automated approach. In the auscultatory approach, a medical professional positions a stethoscope distal to the site of occlusion and listens for Korotkoff sounds which result from the forcing of blood through a partially occluded vessel by the pulsatile pressure of the heart. During cuff deflation, the first of these sounds begins when cuff pressure equals systolic pressure and slowly decays in amplitude until cuff pressure equals diastolic pressure. Several factors affect the accuracy of the auscultatory technique, including the subjects’ clothing, background noise, and the skill of the examiner. Because this technique requires a trained professional, it’s difficult to implement for home monitoring and is also prone to bias from the anxiety subjects experience in clinical settings.

[0082] Several automated methods exist as an alternative to the auscultatory approach. The most common of these are oscillometric methods which utilize the pressure oscillogram extracted during deflation of the pressure cuff. Many algorithms exist for translating this oscillogram to blood pressure values and exhaustive reviews are available elsewhere in the literature. Briefly, the simplest of these algorithms are the maximum amplitude algorithm (MAA) and the derivative approach. The MAA finds the maximum value of the oscillogram and determines the time points of systolic and diastolic pressure on either side of the maximum by utilizing empirical coefficients that specify at which fraction of the maximum amplitude these pressures occur in the oscillogram. These time points are mapped to the original pressure deflation data to calculate systolic and diastolic pressure. The derivative algorithm instead takes the derivative of the oscillogram and identifies systolic and diastolic blood pressure as the maximum and minimum of the derivative, respectively. The derivative algorithm avoids empirical coefficients, but at the cost of the higher noise inherent in the derivative of a signal. Though adopted almost universally, each of these algorithms can convey large errors in pressure values. Variation between subjects, particular disease conditions, and age groups impart an error of 5-10 mmHg onto this standard for blood pressure determination — or approximately 5% of the value being measured. This error may be acceptable for non-medical, wearable markets, but clinical applications would greatly benefit from more accurate blood pressure estimations that allow earlier intervention in patients with rising blood pressure. Several cuffless wearable approaches have emerged in recent years that permit more frequent blood pressure estimates, but the accuracy of these techniques falls short of oscillometric approaches. [0083] Compliance is also a critical parameter of the cardiovascular system and is defined as the change in volume of a vessel relative to a change in pressure. More intuitively, compliance describes a vessel’s ability to stretch. This stretching provides the cardiovascular system with a capacitance, dampening pulses in arteries and permitting expansion in veins for large volume changes. The compliance of the aorta, in particular, ensures steady perfusion of tissue during the diastolic phase of the cardiac cycle. Compliance decreases with age due to the degradation of elastin within vessel walls, and decreased arterial compliance is associated with increased morbidity in aortic valve stenosis, coronary artery disease, and hypertension, itself. Stiffening of vessel walls increases pulsatile pressure, primarily systolic pressure, which ties decreased compliance to the slew of diseases associated with hypertension. The value of arterial compliance as a predictive biomarker approaches that of blood pressure, and this value has garnered substantial interest in measures of compliance.

[0084] Compliance has been historically difficult to determine non-invasively. The most common technique uses pulse wave velocity (PWV), or the speed at which cardiac pressure waves transmit down a vessel. This is performed at two locations, often the carotid and femoral arteries, and calculated as the time delay divided by the distance between the two sensors. The PWV conveys compliance via the Bramwell-Hill equation which relates PWV to pulsatile pressure. The Bramwell-Hill equation may be represented by the following:

[0085]

, where: p represents the density of the blood;

A is the mean area of the blood vessel;

A is the difference between the maximum and minimum area of the blood vessel during a cardiac cycle;

AP is the difference between the central systolic and diastolic pressures;

PWV is the estimated PWV obtained from the measurement of pulsatility (A/1/.4) and pressure variation (AP).

[0086] Inaccuracies appear when comparing the accuracy of PWV compliance estimates to other techniques, and are attributed to errors in blood pressure values input into the Bramwell-Hill equation. Single-sensor arterial tonometry also contains information about the compliance of the underlying vessel, which can be derived by evaluating the shape of the pulsatile waveform. This approach requires empirical data or a trained model, which often becomes inaccurate between subjects and disease states. Imaging techniques can also be used to calculate the changes in vessel diameter associated with a known change in pulsatile pressure, yielding compliance via its pressure-volume relationship. Ultrasound imaging (US) for instance, has been implemented to continuously quantify arterial dilation on a beat-to-beat basis, yielding a change in artery volume assuming radially symmetric distensibility. Though a widespread technology, ultrasound still requires training to use properly and involves image data processing to determine vessel volume changes. Magnetic resonance imaging accommodates the same calculation with increased accuracy, albeit at substantially higher cost. [0087] An improvement in blood pressure estimation accuracy may be possible using NIRS, which measures changes in concentrations of oxyhemoglobin (HbO) and deoxyhemoglobin (Hb) in the blood. When the arm is occluded during standard blood pressure measurements, blood flow is completely blocked distal to the site of occlusion. This results in the steady conversion of HbO to Hb as the occluded tissue continues to metabolize, resulting in a linear decrease in HbO for the period within which cuff pressure exceeds systolic pressure. Blood flow resumes during deflation precisely at the point at which the external cuff pressure equals internal arterial pressure, which is systolic blood pressure. This time point should coincide with a sudden increase in HbO as freshly oxygenated blood flows into the occluded extremity. Measuring this inflection point in HbO therefore communicates systolic blood pressure in healthy patients. In patients with reduced pulsatility, such as LVAD patients, this inflection point should converge on MAP rather than systolic pressure.

[0088] Materials and Methods

[0089] To test this approach, fifteen subjects were recruited for participation in an arm occlusion protocol. This protocol, depicted in FIG. 1A, consisted of between three and twelve rounds of arm occlusion per subject using a standard, manually inflatable blood pressure cuff. For each round of occlusion, the pressure cuff was inflated to approximately 200 mmHg within 10 seconds and deflated to 40 mmHg over a period of 1-2 minutes. Total occlusion time was limited to a maximum of three minutes, with a minimum of two minutes of rest between rounds. This protocol was approved by the Institutional Review Board at Carnegie Mellon University. [0090] Cuff pressure during the procedure was measured using an analog pressure gauge. Pressure values were also digitized via a pressure transducer (BPS BPA, Vernier). The voltage output of this transducer was sampled at 200 Hz by the internal ADC of a microcontroller (Teensy 4.1). AHbO was measured with a commercial NIRS device (Portalite, Artinis) using a single channel with a source-detector distance of 2.9 cm and wavelengths of 760 and 850 nm. The NIRS probe was secured distal to the pressure cuff and adjacent to the brachial artery using self-adhesive wrap. Pressure and NIRS data streams were synchronized using an analog trigger output by the NIRS system.

[0091] The predicted inflection point in AHbO denoting systolic blood pressure was determined by finding the minimum of AHbO in the time window starting with maximum cuff pressure and ending with the maximum peak in AHbO that occurs during reperfusion. A reference blood pressure was necessary to assess the accuracy of the inflection point approach. These reference pressures were obtained using a simple oscillometric approach pressure data from the arm cuff. Briefly, this approach bandpassed data from 0.5-10 Hz, determined heart rate via the FFT of the resulting signal, then bandpassed a second time using a narrower filter with cutoffs at +\- 0.1 Hz of the heart rate. This produced the OWM, whose envelope was calculated via the root mean square of a sliding window with a width of 10 sec. The maximum and minimum slope to the left and right of the peak of the envelope were deemed the time points for systolic and diastolic pressure, respectively. This algorithm was validated on a dataset of 350 OWMs with associated blood pressures. Because pulsatility in the AHbO signal also disappears during occlusion and reappears approximately at systolic blood pressure, the same oscillometric calculation was applied to the optical signal that was used in the cuff pressure signal and compared the resulting blood pressure predictions to the values from standard pressure-based oscillometry.

[0092] In addition to blood pressure, the time traces of AHbO and AHb during and after occlusion may provide features that change at different values of arterial compliance. The height of the peak in AHbO during reperfusion after occlusion, for instance, should be lower for more dilated vessels that exhibit lower compliance due to already being maximally distended. To modulate vascular compliance, arm occlusion in five of the fifteen subjects was performed three times at each of three arm positions: arm above, level with, and below the heart (Figure lb). Given that gravity pulls blood into the arm in a lower position and from the arm in an elevated position, and that this effects the distention and therefore compliance of vessels in the arm, changing arm position should alter compliance. Subjects remained seated throughout the experiment, and in the remaining ten subjects the arm was held at a 90° angle beside the body.

[0093] Results

[0094] The oscillometric algorithm provided clean OWMs and envelopes from which to estimate systolic pressure, diastolic pressure, and MAP (FIG. 5). Applying this algorithm to the associated AHbO time trace also produced reliable OWMs, though with a reduced slope following the peak of the OWM. Testing the algorithm on the validation oscillometric dataset produced a RMSE of 11.4 mmHg (FIG. 6).

[0095] Performing oscillometry on the optical data provided blood pressure values equally accurate to those obtained using the pressure data. This was especially true for MAP and systolic pressure, but optically derived values of diastolic pressure were slightly less accurate (no decrease in pulsatility, FIGS. 3A-3B).

[0096] Several features of the reperfusion curve were examined, and peak height provided the most significant difference across the arm heights and is correlated with changes in arterial compliance. This was only true for oxyhemoglobin. No other features of the curve were found to be significant.

[0097] Discussion

[0098] Though the accuracy here is limited to 10 mmHg, this limitation is likely not a fault of the current protocol, but a limitation imposed by the accuracy of the reference pressure values. The physiological basis for the inflection point method is likely more accurate and should be further verified alongside an invasive blood pressure measurement via arterial line.

[0099] Accuracy is also a factor of cuff inflation and deflation parameters. The data shows a correlation between inflation/deflation rate and pressure estimation bias, indicating that a standard inflation rate and maximal pressure would further improve the accuracy of the optical pressure estimation technique. Dedicated hardware may be beneficial in this respect.

[00100] Optical oscillometry also proved accurate, but primarily for MAP and systolic pressure. This is because the standard pressure oscillogram resembles a “chirp” envelope which decreases towards diastolic pressure, while the optical oscillogram resumes pulsation upon cessation of occlusion. Fortunately, either of these values provides the third given their mathematical relation.

[00101] The physical basis for this technique may improve accuracy when used in tandem with existing oscillometric techniques, especially when compared across subjects and in subjects with disease states that affect vascular pulsatility. Because the pulsatile signal also appears clearly in HbO, oscillometric techniques could also be applied to the optical signal to derive MAP, the most accurately measured value using oscillometric methods, and therefore diastolic pressure using only the optical data. NIRS measures primarily microvasculature and is therefore more agnostic to positioning than pressure transducers, making positioning less of a concern for medical practitioners. [00102] Though the changes are currently only relative, the peak height of the optical reperfusion curve did convey changes in arm height which is believed to be is due to changed arterial compliance.

[00103] Because this method focuses on changes in HbO rather than absolute concentrations, more complex NIRS instruments, such frequency -domain or time-domain NIRS systems, are not necessary. Using relative values (AHbO) simplifies the setup and keeps the required hardware affordable.

[00104] Although embodiments have been described in detail for the purpose of illustration, it is to be understood that such detail is solely for that purpose and that the disclosure is not limited to the disclosed embodiments or aspects, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present disclosure contemplates that, to the extent possible, one or more features of any embodiment or aspect can be combined with one or more features of any other embodiment or aspect.

Claims

THE INVENTION CLAIMED IS

1. A computer- implemented method of determining blood pressure in a patient, comprising: transmitting light into a patient’ s limb distal to an occlusion point; receiving, with at least one processor and from at least one first sensor, first data relating to a pressure applied to occlude a patient’s limb; receiving, with at least one processor and from at least one second sensor, second data relating to a parameter of the transmitted light at a first time point during occlusion of the patient’s limb; receiving, with at least one processor and from the at least one second sensor, third data relating to the parameter of the transmitted light at a second time point after perfusion of the patient’ s limb has resumed; comparing, with at least one processor, the second data to the third data to determine a characteristic of the transmitted light; and determining, with at least one processor, the patient’s systolic blood pressure based at least on the first data and the characteristic of the transmitted light.

2. The method of claim 1, wherein the characteristic is an inflection point in the transmitted light.

3. The method of claim 1 or claim 2, wherein the parameter of the transmitted light corresponds to a change in hemoglobin concentration and/or hemoglobin saturation, optionally to an amount of oxygenated hemoglobin.

4. The method of claim 1, further comprising determining, with at least one processor and with oscillometry, the patient’s blood pressure.

5. The method of any one of claims 1-4, further comprising: receiving, with at least one processor and from the at least one second sensor, data relating to a measure of pulsatility of the patient’ s heart; and determining, with at least one processor and based at least on the measure of pulsatility, the patient’s diastolic blood pressure.

6. The method of any one of claims 1-5, further comprising filtering, with at least one processor, the data relating to the parameter of the transmitted light.

7. The method of any one of claims 1-6, wherein the light source is a nearinfrared light source.

8. The method of claim 7, wherein the transmitted light has a wavelength of about 600 nm to about 800 nm, optionally about 760 nm.

9. The method of claim 7, wherein the transmitted light has a wavelength of about 800 nm to about 2500 nm, optionally about 850 nm.

10. The method of any one of claims 7-9, wherein at the least one sensor is configured to detect light having a wavelength of about 760 nm to about 850 nm.

11. A method of non-invasively determining a patient’s blood pressure, comprising: applying pressure to a patient’s limb at an occlusion point, thereby occluding one or more blood vessels within the limb; arranging a near-infrared light source and a sensor on the patient’s limb distal to the occlusion point; transmitting, with the light source, light into the patient’s limb distal to the occlusion point; receiving, with at least one processor and from at least one first sensor, first data relating to a pressure applied to occlude a patient’s limb; receiving, with at least one processor and from at least one second sensor, second data relating to a parameter of the transmitted light at a first time point during occlusion of the one or more blood vessels within the patient’s limb; reducing the pressure applied to the patient’s limb, thereby allowing perfusion of the patient’s limb distal to the occlusion point; receiving, with at least one processor and from the at least one second sensor, third data relating to the parameter of the transmitted light at a second time point after perfusion of the patient’ s limb has resumed; comparing, with at least one processor, the second data and the third data to determine a characteristic of the transmitted light; and determining, with at least one processor, the patient’s systolic blood pressure based at least on the first data and the characteristic of the transmitted light.

12. The method of claim 11, wherein the characteristic is an inflection point in the transmitted light.

13. The method of claim 11 or claim 12, wherein the parameter of the transmitted light corresponds to an amount of oxygenated hemoglobin.

14. The method of claim 11, further comprising determining, with at least one processor and with oscillometry, the patient’s blood pressure.

15. The method of any one of claims 11-14, further comprising: receiving, with at least one processor and from the at least one second sensor, data relating to a measure of pulsatility of the patient’ s heart; and determining, with at least one processor and based at least on the measure of pulsatility, the patient’s diastolic blood pressure.

16. The method of any one of claims 11-15, further comprising filtering, with at least one processor, the data relating to the parameter of the transmitted light.

17. The method of any one of claims 11-16, wherein the transmitted light has a wavelength of about 600 nm to about 800 nm, optionally about 760 nm.

18. The method of any one of claims 11-16, wherein the transmitted light has a wavelength of about 800 nm to about 2500 nm, optionally 850 nm.

19. The method of any one of claims 11-18, wherein the sensor is configured to detect light having a wavelength of about 760 nm to about 850 nm.

20. A system for determining a patient’ s blood pressure, comprising at least one processor configured to: 1 receive, from at least one first sensor, first data relating to a pressure applied to occlude a patient’s limb; receive, from at least one second sensor, second data relating a parameter of light transmitted into the patient’ s limb at a first time point during occlusion of the patient’ s limb; receive, from the at least one second sensor, third data relating to the parameter of light transmitted into the patient’s limb at a second time point after perfusion of the patient’s limb has resumed; compare the second data to the third data to determine a characteristic of the transmitted light; and determine the patient’s systolic blood pressure based at least on the first data and the characteristic of the transmitted light.

21. The system of claim 20, wherein the at least one processor is further configured to: receive, from the at least one second sensor, fourth data relating the parameter of light transmitted into the patient’s limb at third time point after perfusion of the patient’s limb has resumed; and determine, based at least the fourth data, a measure of pulsatiltiy; and determine, based at least one the measure of pulsatility, the patient’s diastolic blood pressure.

22. The system of claim 20 or claim 21, wherein the at least one processor is further configured to: filter the data relating to the parameter of the transmitted light.

23. The system of any one of claims 20-22, wherein the light that is transmitted into the patient’ s limb is near-infrared light.

24. The system of claim 23, wherein the transmitted light has a wavelength of about 600 nm to about 800 nm, optionally about 760 nm.

25. The system of claim 23, wherein the transmitted light has a wavelength of about 800 nm to about 2500 nm, optionally about 850 nm.

26. The system of any one of claims 23-25, wherein at the least one sensor is configured to detect light having a wavelength of about 760 nm to about 850 nm.

27. A system for determining a patient’s blood pressure, comprising: a pressure cuff; at least one pressure sensor; a near-infrared light source; at least one light sensor configured to detect light having a wavelength of from about 760 nm to about 850 nm; and at least one processor configured to: cause the pressure cuff to apply pressure to a limb of the patient at an occlusion point, thereby occluding blood flow distal to the occlusion point; receive, from the at least one pressure sensor, first data relating to the pressure applied to occlude a patient’s limb; receive, from the at least one light sensor, second data relating a parameter of light transmitted into the patient’s limb at a first time point during occlusion of the patient’s limb; cause the pressure cuff to deflate, thereby releasing pressure on the limb of the patient and allowing blood flow through the occlusion point; receive, from the at least one light sensor, third data relating to the parameter of light transmitted into the patient’ s limb at a second time point after blood flow has resumed through the occlusion point; compare the second data to the third data to determine a characteristic of the transmitted light; and determine the patient’s systolic blood pressure based at least on the first data and the characteristic of the transmitted light.