CN115919312B - Anxiety state data management method and device based on heart rate variability and respiratory variability - Google Patents

Abstract

The application provides a anxiety state data management method and device based on heart rate variability and respiratory variability, which are processed by acquisition and analysis equipment, wherein the method comprises the following steps: acquiring electrocardiosignal data and respiratory waveform signal data of a user; analyzing real-time heart rate analysis data, real-time respiration rate analysis data and real-time comprehensive anxiety state data; and sending the information to the cloud server and the portable man-machine interaction module. The method mainly comprises the steps of acquiring electrocardiosignal data and respiration waveform signal data of a user through acquisition and analysis equipment, analyzing real-time heart rate analysis data, real-time respiration analysis data and real-time comprehensive anxiety state data, and storing the data in a database of a cloud server; and the portable human-computer interaction module displays corresponding heart rate analysis data, respiratory rate analysis data and comprehensive anxiety state data according to the user observation data request. The embodiment of the disclosure can enable a user to easily grasp the anxiety state of the user in daily life.

Description

Management method and device for anxiety state data based on heart rate variability and respiratory variability

Technical Field

The present application relates to the technical field of smart wearable devices, for example, to a method and device for managing anxiety state data based on heart rate variability and respiratory variability.

Background Art

Faced with endless exams to prepare for, endless competitions to participate in, endless work to handle, and an overwhelming amount of big and small things in life... modern people's "anxiety" seems to have reached the point where it permeates the air. Anxiety can bring about a series of adverse reactions in terms of physiology, psychology, and behavior, including feelings of depression, a sense of doom, headaches, difficulty breathing, increased heart rate, gastrointestinal discomfort, increased blood pressure, muscle pain, mental and physical fatigue, sleep disorders, constipation, diarrhea, and even sugar metabolism disorders.

Faced with some anxiety states in life, people are unable to analyze and record their own mental state every day in order to achieve the goal of long-term control of their own health status.

Therefore, there is a lack of a management method for anxiety state data based on heart rate variability and respiratory variability that can allow users to easily understand their own anxiety state in daily life.

Summary of the invention

In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key/critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

The disclosed embodiments provide a method and device for managing anxiety state data based on heart rate variability and respiratory variability, which can enable users to easily understand their own anxiety state in daily life.

In a first aspect, the present application provides a method for managing anxiety state data based on heart rate variability and respiratory variability, which is applied to a collection and analysis device in a management device for anxiety state data based on heart rate variability and respiratory variability, and the method comprises:

Obtain the user's electrocardiogram signal data and respiratory waveform signal data;

Analyze the real-time heart rate analysis data based on the user's ECG signal data;

Analyze the real-time respiratory rate analysis data based on the user's respiratory waveform signal data;

Analyze the real-time comprehensive anxiety status data based on the real-time heart rate analysis data and the real-time respiratory rate analysis data;

Sending a real-time data packet to a cloud server; the real-time data packet includes: real-time heart rate analysis data, real-time respiratory rate analysis data and real-time comprehensive anxiety status data;

Send real-time data packets to the portable human-computer interaction module.

In a second aspect, the present application provides a method for managing anxiety state data based on heart rate variability and respiratory variability, which is applied to a cloud server in a device for managing anxiety state data based on heart rate variability and respiratory variability, and the method comprises:

Establish database;

Receive the real-time heart rate analysis data, real-time respiratory rate analysis data and real-time comprehensive anxiety state data from the calculation and analysis module, and store them in the database as historical heart rate analysis data, historical respiratory rate analysis data and historical comprehensive anxiety state data;

Sending a historical data packet; the historical data packet includes: historical heart rate analysis data, historical respiratory rate analysis data and historical comprehensive anxiety state data.

In a third aspect, the present application provides a method for managing anxiety state data based on heart rate variability and respiratory variability, which is applied to a portable human-computer interaction module in a management device for anxiety state data based on heart rate variability and respiratory variability, and the method comprises:

Receive real-time data packets and historical data packets; receive user observation data requests and display the corresponding real-time data packets or the corresponding historical data packets.

In a fourth aspect, the present application provides a management device for anxiety state data based on heart rate variability and respiratory variability, the device comprising: a collection and analysis device, a cloud server and a portable human-computer interaction module; the collection and analysis device comprises an electrocardiorespiratory collection module and a computing and analysis module; the electrocardiorespiratory collection module is connected to the computing and analysis module, and the computing and analysis module is connected to the cloud server; the portable human-computer interaction module is respectively connected to the computing and analysis module and the cloud server;

The ECG and respiration acquisition module is used to obtain the user's ECG signal data and respiration waveform signal data;

The operation and analysis module is used to parse out real-time heart rate analysis data according to the user's electrocardiogram signal data; parse out real-time respiratory rate analysis data according to the user's respiratory waveform signal data; evaluate real-time comprehensive anxiety state data according to the real-time heart rate analysis data and the real-time respiratory rate analysis data; and send a real-time data packet; the real-time data packet includes: real-time heart rate analysis data, real-time respiratory rate analysis data and real-time comprehensive anxiety state data;

The cloud server is used to establish a database; receive the real-time heart rate analysis data, real-time respiration rate analysis data and real-time comprehensive anxiety state data from the operation and analysis module, and store them in the database as historical heart rate analysis data, historical respiration rate analysis data and historical comprehensive anxiety state data; send a historical data packet; the historical data packet includes: historical heart rate analysis data, historical respiration rate analysis data and historical comprehensive anxiety state data;

The portable human-computer interaction module is used to receive real-time data packets and historical data packets; receive user observation data requests, and display corresponding real-time data packets or corresponding historical data packets.

The present disclosure provides a method and device for managing anxiety state data based on heart rate variability and respiratory variability, which can achieve the following technical effects:

In the disclosed embodiment, the user's ECG signal data and respiratory waveform signal data are mainly acquired through the acquisition and analysis device, and the real-time heart rate analysis data, the real-time heart rate analysis data and the real-time comprehensive anxiety state data are parsed; then, they are stored in the database of the cloud server; finally, the portable human-computer interaction module displays the corresponding heart rate analysis data, the corresponding heart rate analysis data and the corresponding comprehensive anxiety state data according to the user's observation data request. The disclosed embodiment enables users to easily grasp their own anxiety state in daily life.

The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

FIG1 is a schematic diagram of a management device for anxiety state data based on heart rate variability and respiratory variability provided by an embodiment of the present disclosure;

FIG2 is an interactive diagram of a method for managing anxiety state data based on heart rate variability and respiratory variability provided by an embodiment of the present disclosure;

3 is a flow chart of a method for managing anxiety state data based on heart rate variability and respiratory variability applied to a collection and analysis device provided by an embodiment of the present disclosure;

FIG4 is a schematic diagram of a respiratory waveform definition provided by an embodiment of the present disclosure;

5 is a flow chart of another method for managing anxiety state data based on heart rate variability and respiratory variability applied to a cloud server provided by an embodiment of the present disclosure;

6 is a flow chart of another method for managing anxiety state data based on heart rate variability and respiratory variability applied to a portable human-computer interaction module provided by an embodiment of the present disclosure;

FIG7 is a schematic diagram showing the relationship between L and CV_hb of a HBRV triangle provided in an embodiment of the present disclosure;

FIG8 is a schematic diagram of a HBRV triangle provided in an embodiment of the present disclosure.

DETAILED DESCRIPTION

In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

The following description and accompanying drawings fully illustrate specific embodiments of the present invention so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The examples represent possible changes only. Unless explicitly required, separate components and functions are optional, and the order of operations may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. The scope of the embodiments of the present invention includes the entire scope of the claims, and all available equivalents of the claims. In this article, each embodiment may be represented individually or generally by the term "invention", which is only for convenience, and if more than one invention is disclosed in fact, it is not intended to automatically limit the scope of the application to any single invention or inventive concept. In this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, without requiring or implying any actual relationship or order between these entities or operations. Moreover, the terms "comprises", "includes" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the presence of other identical elements in the process, method or device including the elements. The various embodiments herein are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other. For the methods, products, etc. disclosed in the embodiments, since they correspond to the method part disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part description.

In addition, the terms "disposed", "connected", and "fixed" should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection, or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be an internal connection between two devices, elements, or components. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present disclosure can be understood according to specific circumstances.

As shown in FIG1 , the present application provides a management device for anxiety state data based on heart rate variability and respiratory variability, the device comprising: an acquisition and analysis device, a cloud server and a portable human-computer interaction module; the acquisition and analysis device comprises an electrocardiorespiratory acquisition module and an operation and analysis module; the electrocardiorespiratory acquisition module is connected to the operation and analysis module, and the operation and analysis module is connected to the cloud server; the portable human-computer interaction module is connected to the operation and analysis module and the cloud server, respectively. The electrocardiorespiratory acquisition module is used to obtain the user's electrocardiosignal data and respiratory waveform signal data. The operation and analysis module is used to parse out real-time heart rate analysis data according to the user's electrocardiosignal data; parse out real-time respiratory rate analysis data according to the user's respiratory waveform signal data; evaluate real-time comprehensive anxiety state data according to real-time heart rate analysis data and real-time respiratory rate analysis data; send a real-time data packet; the real-time data packet comprises: real-time heart rate analysis data, real-time respiratory rate analysis data and real-time comprehensive anxiety state data. The cloud server is used to establish a database; receive the real-time heart rate analysis data, real-time respiration rate analysis data and real-time comprehensive anxiety state data from the operation and analysis module, and store them in the database as historical heart rate analysis data, historical respiration rate analysis data and historical comprehensive anxiety state data; send historical data packets; the historical data packets include: historical heart rate analysis data, historical respiration rate analysis data and historical comprehensive anxiety state data. The portable human-computer interaction module is used to receive real-time data packets and historical data packets; receive user observation data requests, and display the corresponding real-time data packets or the corresponding historical data packets.

It should be understood that the data packet includes heart rate analysis data, respiratory rate analysis data and corresponding comprehensive anxiety state data. After the real-time heart rate analysis data, real-time respiratory rate analysis data and real-time comprehensive anxiety state data are stored in the database, they will become historical heart rate analysis data, historical respiratory rate analysis data and historical comprehensive anxiety state data.

In practical applications, the portable human-computer interaction module and the computing and analysis module can be connected by Bluetooth wireless connection. The portable human-computer interaction module and the computing and analysis module can be connected by Bluetooth wireless connection. It should be understood that the cloud server is connected to the collection and analysis device and the portable human-computer interaction module through the network. The portable human-computer interaction module can be a CNC touch screen with input and output functions. The user can input a user observation data request, and the CNC touch screen then outputs the corresponding data packet.

Furthermore, the device also includes a printing module; the printing module is connected to the portable human-computer interaction module.

The printing module may be a printer, which prints out the content of the data packet according to the data packet requested by the user's observation data, and displays the content of the data packet more intuitively.

See FIG. 2 , which is an interactive diagram of a method for managing anxiety state data based on heart rate variability and respiratory variability.

In conjunction with FIG. 3 , an embodiment of the present disclosure provides a method for managing anxiety state data based on heart rate variability and respiratory variability, which is applied to a collection and analysis device in a management device for anxiety state data based on heart rate variability and respiratory variability, and the method includes:

S210, the collection and analysis device obtains the user's electrocardiogram signal data and respiratory waveform signal data.

S220, the collection and analysis device parses the user's electrocardiogram signal data to obtain real-time heart rate analysis data.

S230, the collection and analysis device analyzes the real-time respiratory rate analysis data according to the user's respiratory waveform signal data.

S240, the collection and analysis device parses the real-time comprehensive anxiety status data based on the real-time heart rate analysis data and the real-time respiratory rate analysis data.

S250, sending a real-time data packet to a cloud server; the real-time data packet includes: real-time heart rate analysis data, real-time respiratory rate analysis data and real-time comprehensive anxiety status data.

S260, sending a real-time data packet to a portable human-computer interaction module.

Furthermore, the ECG signal data includes: SDNN, HRV_TI, SDANN, RMSSD, total heart rate THB, ECG NN interval mean; the heart rate analysis data is the heart rate variation coefficient CV_h;

Analyze real-time heart rate analysis data, including:

According to formula (1), the heart rate variability trend TV_h is calculated.

Where X is the calibration factor.

In practical applications, due to the different interferences, age, body mass index, etc. of the subjects in the resting state, the X coefficient is introduced, which is The value range of N is [0.15-1].

According to formula (2), the heart rate variation coefficient CV_h is calculated:

Furthermore, the respiratory waveform signal data includes the respiratory cycle TT, the respiratory cycle average value SDTT, BRV_TI, SDATT, RMSSDTT.

See FIG4 , which is a schematic diagram of a respiratory waveform definition in this application.

Wherein, SDTT is the standard deviation of all respiratory cycles, calculated as follows:

SDATT is to divide all recorded respiratory cycles into several time periods in 5-minute intervals according to the recorded time sequence. First, calculate the average value of the respiratory cycle in each 5-minute interval, and then calculate the standard deviation of these average values, according to the following formula (4):

Among them, N is the evaluation time, which is N 5 minutes. is the mean TT value of the i-th 5-minute respiratory cycle, For N The mean of .

RMSSDTT is the root mean square value of the difference between adjacent breathing cycles throughout the entire process, calculated according to the following formula (5):

BRV_TI is the total number of breathing cycles divided by the height of the breathing cycle histogram.

The respiratory rate analysis data is the respiratory variation coefficient CV_b;

Analyze real-time respiratory rate analysis data, including:

According to formula (6), the mean respiratory rate variation AVG_b is calculated.

According to formula (7), the coefficient of variation of respiratory rate CV_b is calculated:

In the design of the respiratory rate variability algorithm model, the CV_b value calculated using a single SDTT is quite different from the actual state of the sympathetic nervous system, and the CV_b calculated using a single value of SDATT, RMSSDTT, and BRV_TI is also the same. After continuous adjustment and optimization of the model, an objective and accurate CV_b needs to be calculated by combining SDTT, SDATT, RMSSDTT, BRV_TI, and the mean of the respiratory cycle.

Further, the comprehensive anxiety state data is the cardiopulmonary coefficient of variation CV_hb;

Analyze the real-time comprehensive anxiety status data, including:

The cardiopulmonary coefficient of variation is calculated according to formula (8):

CV_hb＝((1-CV_h)*a+CV_b*(1-a))*100 (8)

Among them, a is the cardiopulmonary weight coefficient, and its value is in the range of (0,1).

The cardiopulmonary variation coefficient CV_hb is modeled according to the close correlation between heart rate variability, respiratory rate variability and sympathetic and parasympathetic nerves. The above CV_hb is optimized based on a large amount of data. Combined with a large number of data samples, when CV_hb≤0.68, the anxiety level of the subject is very low, the parasympathetic nerves are dominant, and the body and mind are very relaxed; when 0.68＜CV_hb≤3.25, the anxiety level of the subject is low, at this time the sympathetic nervous system and the parasympathetic nervous system are temporarily in a state of balance, and the subject is relatively relaxed physically and mentally; when 3.25＜CV_hb≤13, the anxiety level of the subject is high, the sympathetic nervous system is dominant, and the subject is more anxious and nervous; when CV_hb＞13, the anxiety level of the subject is very high, the sympathetic nervous system is absolutely dominant, and the subject is very anxious and nervous.

As shown in FIG5 , a method for managing anxiety state data based on heart rate variability and respiratory variability is applied to a cloud server in the device for managing anxiety state data based on heart rate variability and respiratory variability. The method includes:

S310, the cloud server establishes a database.

S320, the cloud server receives the real-time heart rate analysis data, real-time respiratory rate analysis data and real-time comprehensive anxiety status data from the calculation and analysis module, and stores them in the database as historical heart rate analysis data, historical respiratory rate analysis data and historical comprehensive anxiety status data.

S330, the cloud server sends a historical data packet; the historical data packet includes: historical heart rate analysis data, historical respiratory rate analysis data and historical comprehensive anxiety state data.

As shown in FIG6 , a method for managing anxiety state data based on heart rate variability and respiratory variability is applied to a portable human-computer interaction module in the device for managing anxiety state data based on heart rate variability and respiratory variability. The method includes:

S410, the portable human-computer interaction module receives a real-time data packet and a historical data packet.

S420, the portable human-computer interaction module receives a user observation data request and displays a corresponding real-time data packet or a corresponding historical data packet.

Furthermore, the ECG signal data includes: SDNN, HRV_TI, SDANN, RMSSD, total heart rate THB, ECG NN interval mean; the heart rate analysis data is the heart rate variation coefficient CV_h;

Analyze real-time heart rate analysis data, including:

According to formula (1), the heart rate variability trend TV_h is calculated.

Where X is the calibration coefficient;

According to formula (2), the heart rate variation coefficient CV_h is calculated:

A specific embodiment may be to use an acquisition and analysis device to obtain the user's electrocardiogram signal data and respiratory waveform signal data.

Specifically, the user's ECG signal data for 15 minutes, 20 minutes, 25 minutes and 30 minutes are collected. The ECG signal data includes SDNN, HRV_TI (HRVTriangular Index), SDANN, RMSSD, total heart rate THB, and ECG NN interval mean. Due to the different interferences, age, body mass index, etc. of the user in the resting state, the X coefficient is introduced, which is:

Among them, the value range of N is [0.15-1].

The user's breathing waveform signal data of 15 minutes, 20 minutes, 25 minutes and 30 minutes are collected. The breathing waveform signal data includes SDTT, BRV_TI, SDATT, and RMSSDTT.

According to the calculation of CV_h of the present invention, combined with the user's subjective comments and physical fatigue recovery test, the correlation between CV_h and anxiety is verified. When CV_h is high, the anxiety level is low, and the user is in a relaxed state. When the CV_h value is less than 20, the user's anxiety level is high. See Table 1 for the corresponding user CV_h evaluation value. Among them, max_rr is the maximum value of the RR interval of the ECG waveform, min_rr is the minimum value of the RR interval of the ECG waveform, and ave_rr is the average value of the RR interval of the ECG waveform.

Table 1

id SDNN SDANN RMSSD HRV_ti heartbeat_num max_rr min_rr ave_rr X TV_h CV_h 1 27.00 13.00 18.00 7.00 2255.00 905.00 625.00 797.00 0.40 106.72 13.39 2 34.00 17.00 21.00 7.00 2077.00 1060.00 440.00 865.00 0.45 135.46 15.66 3 37.00 20.00 26.00 9.00 2102.00 1085.00 650.00 855.00 0.30 149.24 17.45 4 34.00 15.00 24.00 8.00 2237.00 980.00 525.00 803.00 0.35 134.76 16.78 5 53.00 27.00 34.00 11.00 2164.00 1605.00 540.00 830.00 0.50 298.43 35.96 6 41.00 19.00 29.00 9.00 2052.00 1095.00 565.00 876.00 0.48 205.27 23.43 7 39.00 22.00 17.00 7.00 2183.00 2260.00 610.00 823.00 0.37 131.06 15.92 8 39.00 18.00 16.00 7.00 2191.00 1495.00 615.00 821.00 0.40 123.24 15.01 9 28.00 19.00 11.00 7.00 2123.00 1065.00 530.00 847.00 0.54 109.92 12.98 10 58.00 63.00 24.00 8.00 2257.00 1525.00 475.00 796.00 0.60 315.39 39.62

The respiratory waveform signal data includes SDTT, BRV_TI, SDATT, and RMSSDTT.

Calculate the coefficient of variation of respiratory rate CV_b. Specifically, according to the formula

and

formula Calculate CV_b. According to data regression analysis, it is found that CV_b is negatively correlated with anxiety level, that is, when the CV_b value is large, the user's anxiety level is high. When the anxiety level is low or relaxed, the CV_b value should be less than or equal to 50.

See Table 2 for the user's CV_b evaluation value.

Table 2

ID SDTT SDATT RMSSDTT BRV_TI RESP AVG_b CV_b 1 1221.00 476.00 789.00 32.00 20.00 12.54 62.72 2 1304.00 555.00 673.00 31.00 19.00 12.66 66.61 3 884.00 427.00 643.00 18.00 21.00 11.10 52.87 4 1270.00 498.00 771.00 25.00 22.00 12.66 57.54 5 756.00 396.00 650.00 16.00 18.00 10.66 59.22 6 657.00 406.00 554.00 20.00 19.00 10.11 53.24 7 784.00 398.00 640.00 16.00 21.00 10.72 51.04 8 904.00 415.00 680.00 24.00 20.00 11.24 56.22 9 875.00 409.00 711.00 17.00 24.00 11.21 46.72 10 889.00 456.00 685.00 17.00 20.00 11.31 56.55

Calculate the cardiopulmonary coefficient of variation CV_hb. The anxiety level can be assessed from the indexes of the heart rate coefficient of variation CV_h and the respiratory rate coefficient of variation CV_b. The present invention creatively proposes the cardiopulmonary coefficient of variation CV_hb.

The CV_hb expression is: CV_hb=((1-CV_h)*a+CV_b*(1-a))*100.

The acquisition and analysis device sends the calculated heart rate variability coefficient CV_h, respiratory rate variability coefficient CV_b and cardiopulmonary variability coefficient CV_hb as the heart rate variability coefficient CV_h, real-time respiratory rate variability coefficient CV_b and real-time cardiopulmonary variability coefficient CV_hb to the cloud server and the portable human-computer interaction module.

The portable human-computer interaction module is a touch panel.

The user inputs instructions for displaying the real-time heart rate variability coefficient CV_h, the real-time respiratory rate variability coefficient CV_b, and the real-time cardiopulmonary variability coefficient CV_hb through the touch panel.

The touch panel is displayed in the form of an HBRV triangle.

The HBRV triangle of the cardiopulmonary variation coefficient CV_hb is composed of CV_h and CV_b. The two vertical sides of the right triangle are deduced from the formula CV_hb=((1-CV_h)*a+CV_b*(1-a))*100, which together form a vector triangle. The triangle contains parameters CV_h _△ , CV_b _△ , L _△ , angle α, and area S.

The HBRV triangle is constructed by the cardiopulmonary variability coefficient CV_hb, where the vertical axis is the heart rate variability HRV: (1-CV_h)*a*100, and the horizontal axis is the respiratory variability: BRV part CV_b*(1-a)*100, so as to calculate the area S, hypotenuse length L, and angle change α of the corresponding HBRV triangle;

In the HBRV triangle:

CV_h _△ = (1-CV_h)*a*100, where the coefficient a ranges from [0-1], and usually a is 0.5;

The CV_h is negatively correlated with the anxiety level, forming a right-angled side of the triangle, while the anxiety level is positively correlated with CV_hb, through

The triangle constructed by CV_h and CV_hb is multiplied by a coefficient (1-CV_h)*a to reflect the true evaluation value of the anxiety level.

CV_b _△ =CV_b*(1-a)*100, where the coefficient a ranges from [0-1], and usually a is 0.5;

The CV_b is positively correlated with the anxiety level, constructing the other right-angled side of the triangle, while the anxiety level is positively correlated with CV_hb. The triangle constructed by CV_b and CV_hb is multiplied by the coefficient (1-a)*CV_h to comply with the evaluation principle and application of the anxiety level.

L2 _△ = (CV_h _△ ) ² + (CV_b _△ ) ² ;

The hypotenuse L _△ is calculated from the right-angled sides CV_h _△ and CV_b _△ , and the hypotenuse L _△ is positively correlated with the anxiety level. When L _△ is at a critical point, the sympathetic nervous system and the parasympathetic nervous system are just balanced to measure the user's anxiety level.

See Figure 7, which is a schematic diagram of the relationship between L and CV_hb. See Figure 8, which is a schematic diagram of the HBRV triangle.

Where, α = arc sin (CV_h _△ / L _△ );

The angle α reflects the relationship between heart rate variability or respiratory variability and anxiety level. When α is large, that is, α＞45°, it means that heart rate variability dominates the anxiety level, and it is a negative correlation, that is, the larger the α is, the lower the anxiety level, and vice versa; when α is small, that is, α＜45°, the respiratory variability dominates the anxiety level, and the smaller α is, the higher the anxiety level; when α＝45°, the effects of heart rate variability and respiratory rate variability on anxiety level are equal, and the anxiety level value at this time is higher.

S＝1/2*(CV_h _△ *CV_b _△ );

The HBRV triangle area S is used to judge the anxiety level based on the full-course evaluation St. At the same time, the HBRV triangle area Sn is extracted with an integer multiple of 5 minutes, which can reflect the discreteness and distribution state of the HBRV triangle.

When the area S1 to Sn is gradually raised, the discrete type is high and the anxiety level is on the rise. When the area S1 to Sn is gradually reduced, the discrete type is low and the anxiety level is on the decline. When the area S1 to Sn is relatively constant, the anxiety level is relatively stable, the user's cardiopulmonary variability is stable, the cardiopulmonary function is good, and the mood is stable.

The HBRV model of the present invention is constructed by using the above CV_h and CV_b to construct the two sides of the right triangle. When the HBRV triangle area S is larger, it means that the cardiopulmonary variability coefficient is larger and the anxiety level is lower. On the contrary, the anxiety level is higher.

When the angle α is larger, it means that the user's HRV heart rate variability is greater, and the heart rate variability occupies a dominant position in the HBRV triangle, that is, the anxiety level. Conversely, it means that the user's BRV respiratory rate variability is greater, and the respiratory rate variability occupies a dominant position in the HBRV triangle, that is, the anxiety level.

When the user performs the HBRV anxiety level test of the method, data collection is performed for at least 15 minutes in a resting state. The present invention will also use 5 minutes as the dividing line to calculate S1, S2, and S3 of the HBRV triangle respectively. When S1 to S3 gradually increase, it means that the user's anxiety level is high, otherwise the anxiety level is low.

At the same time, users can conduct an evaluation lasting more than 15 minutes. The model will automatically calculate S1, S2, ...Sn, and plot the trend of each S1, S2, ...Sn. When S gradually increases, it means that the user's anxiety level is high, otherwise it means that the user is in a good state.

The above data model only performs statistical analysis on users. When big data analysis and statistics are performed on different users, it is necessary to multiply the user's age, gender, BMI, resting heart rate and respiratory rate, and major psychological events that have occurred to the user recently by coefficient A to divide the anxiety level of the general population.

The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and/or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and/or comprising refer to the presence of stated features, wholes, steps, operations, elements, and/or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and/or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the above-described devices, devices and units may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

The flowchart and block diagram in the accompanying drawings show the possible architecture, functions and operations of the device, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts, may be implemented by dedicated hardware-based devices that perform the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims (7)

1. A method for managing anxiety state data based on heart rate variability and respiratory variability, characterized in that it is applied to a collection and analysis device in a management device for anxiety state data based on heart rate variability and respiratory variability, and the method comprises: Obtain the user's electrocardiogram signal data and respiratory waveform signal data; Analyze the real-time heart rate analysis data based on the user's ECG signal data; Analyze the real-time respiratory rate analysis data based on the user's respiratory waveform signal data; Analyze the real-time comprehensive anxiety status data based on the real-time heart rate analysis data and the real-time respiratory rate analysis data; Sending a real-time data packet to a cloud server; the real-time data packet includes: real-time heart rate analysis data, real-time respiratory rate analysis data and real-time comprehensive anxiety status data; sending real-time data packets to a portable human-computer interaction module; The ECG signal data includes: SDNN, triangle index HRV_TI, SDANN, RMSSD, total heartbeat number THB and ECG NN interval mean; the heart rate analysis data is the heart rate variation coefficient CV_h; Analyze real-time heart rate analysis data, including: According to formula (1), the heart rate variability trend TV_h is calculated. Where X is the calibration coefficient; According to formula (2), the heart rate variation coefficient CV_h is calculated: The respiratory waveform signal data includes the respiratory cycle TT, the respiratory cycle average value SDTT, BRV_TI, SDATT, RMSSDTT; Among them, SDTT is the standard deviation of all respiratory cycles, which is calculated according to the following formula (3): SDATT is to divide all recorded respiratory cycles into several time periods in 5-minute intervals according to the recorded time sequence. First, calculate the average value of the respiratory cycle in each 5-minute interval, and then calculate the standard deviation of these average values, according to the following formula (4): Among them, N is the evaluation time, which is N 5 minutes. is the mean TT value of the i-th 5-minute respiratory cycle, For N The mean of RMSSDTT is the root mean square value of the difference between adjacent breathing cycles throughout the entire process, calculated according to the following formula (5): BRV_TI is the total number of respiratory cycles divided by the height of the respiratory cycle histogram; The respiratory rate analysis data is the respiratory variation coefficient CV_b; Analyze real-time respiratory rate analysis data, including: According to formula (6), the mean respiratory rate variation AVG_b is calculated. According to formula (7), the coefficient of variation of respiratory rate CV_b is calculated: 2. The method according to claim 1, characterized in that the comprehensive anxiety state data is the cardiopulmonary coefficient of variation CV_hb; Analyze the real-time comprehensive anxiety status data, including: The cardiopulmonary coefficient of variation is calculated according to formula (8): CV_hb＝((1-CV_h)*a+CV_b*(1-a))*100 (8) Among them, a is the cardiopulmonary weight coefficient, and its value is in the range of (0,1). 3. A method for managing anxiety state data based on heart rate variability and respiratory variability, characterized in that it is applied to a portable human-computer interaction module in a device for managing anxiety state data based on heart rate variability and respiratory variability, and the method comprises: Receive real-time data packets and historical data packets; receive user observation data requests and display corresponding real-time data packets or corresponding historical data packets; The real-time data packet includes: real-time heart rate analysis data, real-time respiratory rate analysis data and real-time comprehensive anxiety state data; The real-time heart rate analysis data is analyzed based on the user's ECG signal data, including: SDNN, triangle index HRV_TI, SDANN, RMSSD, total heart beat count THB and ECG NN interval mean; the heart rate analysis data is the heart rate variation coefficient CV_h; Analyze real-time heart rate analysis data, including: According to formula (1), the heart rate variability trend TV_h is calculated. Where X is the calibration coefficient; According to formula (2), the heart rate variation coefficient CV_h is calculated: Real-time respiratory rate analysis data is analyzed based on the user's respiratory waveform signal data; The respiratory waveform signal data includes respiratory cycle TT, respiratory cycle average TT, SDTT, BRV_TI, SDATT, and RMSSDTT; Among them, SDTT is the standard deviation of all respiratory cycles, which is calculated according to the following formula (3): SDATT is to divide all recorded respiratory cycles into several time periods in 5-minute intervals according to the recorded time sequence. First, calculate the average value of the respiratory cycle in each 5-minute interval, and then calculate the standard deviation of these average values, according to the following formula (4): Among them, N is the evaluation time, which is N 5 minutes. is the mean TT value of the i-th 5-minute respiratory cycle, For N The mean of RMSSDTT is the root mean square value of the difference between adjacent breathing cycles throughout the entire process, calculated according to the following formula (5): BRV_TI is the total number of respiratory cycles divided by the height of the respiratory cycle histogram; The respiratory rate analysis data is the respiratory variation coefficient CV_b; Analyze real-time respiratory rate analysis data, including: According to formula (6), the mean respiratory rate variation AVG_b is calculated. According to formula (7), the coefficient of variation of respiratory rate CV_b is calculated: The comprehensive anxiety state data is the cardiopulmonary coefficient of variation CV_hb; The analysis process of real-time comprehensive anxiety state data includes: The cardiopulmonary coefficient of variation is calculated according to formula (8): CV_hb＝((1-CV_h)*a+CV_b*(1-a))*100 (8) Among them, a is the cardiopulmonary weight coefficient, and its value is in the range of (0,1). 4. The method according to claim 3, characterized in that when displaying the cardiopulmonary coefficient of variation CV_hb in the corresponding real-time data packet or the corresponding historical data packet, it is displayed in the form of a HBRV triangle; Displayed in the form of HBRV triangle, including: CV_h and CV_b are used as two right-angle sides to construct the HBRV triangle, where the area S of the HBRV triangle is positively correlated with the cardiopulmonary coefficient of variation. 5. A management device for anxiety state data based on heart rate variability and respiratory variability, characterized in that the device comprises: a collection and analysis device, a cloud server and a portable human-computer interaction module; the collection and analysis device comprises an electrocardiorespiratory collection module and an operation and analysis module; the electrocardiorespiratory collection module is connected to the operation and analysis module, and the operation and analysis module is connected to the cloud server; the portable human-computer interaction module is respectively connected to the operation and analysis module and the cloud server; The ECG and respiration acquisition module is used to obtain the user's ECG signal data and respiration waveform signal data; An operation and analysis module, executing the method as claimed in claim 1 or 2; The cloud server is used to establish a database; receive the real-time heart rate analysis data, real-time respiration rate analysis data and real-time comprehensive anxiety state data from the operation and analysis module, and store them in the database as historical heart rate analysis data, historical respiration rate analysis data and historical comprehensive anxiety state data; send a historical data packet; the historical data packet includes: historical heart rate analysis data, historical respiration rate analysis data and historical comprehensive anxiety state data; A portable human-computer interaction module, executing the method as claimed in claim 3 or 4. 6. The device according to claim 5 is characterized in that the device further comprises a printing module, and the printing module is connected to the portable human-computer interaction module. 7. The device according to claim 5 is characterized in that the portable human-computer interaction module and the operation and analysis module are connected by Bluetooth wireless connection.