WO2018129822A1

WO2018129822A1 - Method for measuring blood pressure and device

Info

Publication number: WO2018129822A1
Application number: PCT/CN2017/080811
Authority: WO
Inventors: 李靖; 卢恒惠; 吴黄伟
Original assignee: 华为技术有限公司
Priority date: 2017-01-10
Filing date: 2017-04-17
Publication date: 2018-07-19
Also published as: CN108697350A

Abstract

A method for measuring blood pressure and a device, the method comprising: a measuring device extracting feature data from a biological signal according to a collected biological signal of a user; calculating a classification reference value according to the feature data and preset classification parameters; determining a target blood pressure prediction model from a blood pressure prediction model set according to a preset threshold value range in which the classification reference value falls; inputting the feature data into the target blood pressure prediction model to obtain a blood pressure measurement result of the user. Therefore, the method for measuring blood pressure provided by the present application may better adapt to diverse blood pressures, and feature data is no longer directly inputted into a single blood pressure prediction model, which may effectively improve the accuracy of blood pressure measurement.

Description

Blood pressure measuring method and device

This application claims the priority of the Chinese Patent Application, filed on Jan. 10, 2017, with the application number No. 201710017279.8, entitled "A Classification-Based Blood Pressure Measurement Method and Terminal", the entire contents of which are incorporated by reference. In this application.

Technical field

The present application relates to the field of terminals, and in particular, to a blood pressure measuring method and device.

Background technique

Blood pressure is the driving force that circulates blood in the blood vessels, providing enough blood for each tissue to maintain normal metabolism. Among them, high blood pressure showed high blood pressure, is a very common cardiovascular disease, there are about 160 million high blood pressure patients in China, high blood pressure will bring many hazards such as stroke, blindness and myocardial infarction.

In recent years, with the development of mobile health services, the convenience of blood pressure monitoring is gradually increasing, and more convenient wearable devices such as watches and bracelets have been able to achieve convenient blood pressure measurement. How to improve the accuracy of these wearable device blood pressure measurements becomes an important issue.

Specifically, the wearable device measures the blood pressure based on the acquired biological signal, and the working principle is:

1) acquiring a biological signal, and acquiring a biological signal related to the user, such as a cardiac signal such as an electrocardiogram or a pulse wave, through a biosignal acquisition module in the wearable device;

2) Establish a predictive model, based on a priori calculation of the theoretical formula of blood pressure, or pre-acquired data to establish a model of blood pressure prediction;

3) extracting feature data, performing pre-processing operations such as filtering the collected biosignal, and then extracting relevant feature data capable of characterizing the biosignal from the biosignal after performing the pre-processing operation;

4) Input the extracted feature data into the prediction model in 2), and output the predicted blood pressure value.

It can be seen from the above that the existing wearable blood pressure device preprocesses the biosignal to obtain the corresponding feature data when measuring the blood pressure, and then directly inputs the feature data into a single prediction model for blood pressure prediction, but a single The predictive model is not suitable for the diversity of blood pressure, and the need to accurately predict blood pressure is not achieved, resulting in low accuracy of blood pressure measurement.

Summary of the invention

The embodiment of the present application provides a blood pressure measuring method and device for solving the problem that the blood pressure measurement accuracy is not high.

In a first aspect, the present application provides a blood pressure measurement method, including: a measurement device extracts feature data in a biosignal according to a collected biosignal of a user, and calculates a classification reference value according to the feature data and a preset classification parameter, and then according to the classification The preset threshold range into which the reference value falls, the target blood pressure prediction model is determined from the blood pressure prediction model set, and finally the feature data is input into the target blood pressure prediction model to obtain the user's blood pressure measurement result. Each of the blood pressure preset models corresponds to a preset threshold range, and the preset threshold range corresponding to the target blood pressure prediction model is the same as the preset threshold range in which the classification reference value falls. Therefore, using the method provided by the present application, the measuring device collects the user's biological signal, extracts the feature data from the biological signal, and calculates the classification reference value according to the preset classification parameter and the feature data extracted from the biological signal. When the preset threshold range in which the classification reference value falls is different, the target blood pressure prediction model determined by the measuring device is also different. Therefore, the blood pressure measurement method provided by the present application can better adapt to the scene of blood pressure diversity, and the feature is not directly used. The data is input to a single blood pressure prediction model, which can effectively improve the accuracy of blood pressure measurement.

In a possible implementation manner, the preset classification parameter is obtained by the following method: the measurement device acquires the training data set, and divides the training data set into at least two according to the actual blood pressure measurement result included in each training data. A type of training data subset obtains preset classification parameters according to at least two first type training data subsets and a preset classification function. For example, the preset classification function may be a logistic regression function. Wherein, each training data in the training data set includes a characteristic data and an actual blood pressure measurement result, and the actual blood pressure measurement result includes systolic blood pressure and diastolic blood pressure, and the biosignal corresponding to the characteristic data in each training data and the actual blood pressure measurement result The corresponding biosignals are the same. It should be understood that the training data set includes a large amount of training data, the feature data in the kth training data is the feature data extracted for the kth biological signal, and the actual blood pressure measurement result in the kth training data is for the first The blood pressure measurement result of k biosignals, the actual blood pressure measurement result herein may refer to a blood pressure measurement result measured by a user using a tool such as a mercury sphygmomanometer or a cuff sphygmomanometer, and k is a positive integer. It should be understood that when the preset threshold range includes two or more, two or more preset classification parameters are needed, and the two or more classification reference values that need to be calculated are combined to respectively correspond to different preset threshold ranges.

In one possible implementation, the blood pressure prediction model set is obtained by the following method:

The measuring device establishes a blood pressure prediction model according to each of the first type of training data subsets, and obtains a blood pressure prediction model set. Therefore, the measuring device can directly establish a blood pressure prediction model according to the first type of training data subset, and the method is simple.

The measuring device divides the training data set into at least two second types of training data subsets according to actual blood pressure measurement results included in each training data, the number of the first type of training data subsets and the second type of training data subsets The number is the same; a blood pressure prediction model is established according to each of the second type of training data subsets, and a blood pressure prediction model set is obtained. Therefore, the measuring device can establish a blood pressure prediction model by using a second subset of training data subsets different from the first type of training data subset, and the method is flexible to suit the needs of different scenarios.

In a possible implementation manner, the number of the first type of training data subset is the same as the number of the second type training data subset, and each of the first type training data subsets corresponds to a first type of actual blood pressure measurement result. Range, each second type of training data subset corresponds to a second type of actual blood pressure measurement result range, and the i-th first type of training data subset corresponding to the i-th first type of actual blood pressure measurement result range is A subset of the range of values of the i-th second type of actual blood pressure measurement corresponding to the i-th second-type training data subset, wherein i is a positive integer. Therefore, the reason for dividing the training data set into at least two second types of training data subsets and not continuing to use at least two first type training data subsets is that the training data subsets used to establish different blood pressure prediction models are There is a certain degree of redundancy between them to ensure the accuracy of blood pressure measurements near the classification nodes.

In a second aspect, the present application provides a blood pressure measuring device, including: a collector, a memory, and a processor, wherein the collector is configured to collect a biosignal of the user; the memory is configured to store the computer program; and the processor is configured to invoke The computer program stored in the memory is executed to: extract feature data in the biosignal according to the biosignal of the user collected by the collector; calculate the classification reference value according to the feature data and the preset classification parameter; and preset according to the classification reference value a threshold range, wherein the target blood pressure prediction model is determined from the blood pressure prediction model set, each blood pressure preset model corresponding to a preset threshold range, and the preset threshold range corresponding to the target blood pressure prediction model is the same as the preset threshold range in which the classification reference value falls The characteristic data is input into the target blood pressure prediction model to obtain the blood pressure measurement result of the user.

In a possible implementation, the processor is further configured to: acquire a training data set, and train the data set Each piece of training data includes one feature data and one actual blood pressure measurement result, and the biosignal corresponding to the feature data in each training data is the same as the biosignal corresponding to the actual blood pressure measurement result; according to the actual blood pressure measurement result included in each training data And dividing the training data set into at least two first types of training data subsets; and obtaining preset classification parameters according to at least two first type training data subsets and a preset classification function.

In a possible implementation, the processor is further configured to: establish a blood pressure prediction model according to each of the first types of training data subsets, and obtain a blood pressure prediction model set.

In a possible implementation, the processor is further configured to: divide the training data set into at least two second types of training data subsets according to actual blood pressure measurement results included in each training data, and the first type of training The number of data subsets is the same as the number of the second type of training data subsets; a blood pressure prediction model is established according to each second type of training data subset to obtain a blood pressure prediction model set.

In a possible implementation manner, the number of the first type of training data subset is the same as the number of the second type training data subset, and each of the first type training data subsets corresponds to a first type of actual blood pressure measurement result. Range, each second type of training data subset corresponds to a second type of actual blood pressure measurement result range, and the i-th first type of training data subset corresponding to the i-th first type of actual blood pressure measurement result range is A subset of the range of values of the i-th second type of actual blood pressure measurement corresponding to the i-th second-type training data subset, wherein i is a positive integer.

In a third aspect, the present application provides a computer readable storage medium having stored therein instructions that, when executed on a computer, cause the computer to perform the methods described in the various aspects above.

In a fourth aspect, the present application provides a computer program product comprising instructions for causing a computer to perform the methods described in the above aspects when the computer program product runs on a computer.

DRAWINGS

1 is a schematic view of a wristwatch having a blood pressure measuring function in an embodiment of the present application;

2 is a schematic diagram of a basic structure of a measuring device in an embodiment of the present application;

3 is a flow chart showing an overview of a blood pressure measuring method in an embodiment of the present application;

4 is a specific flowchart of a blood pressure measurement performed by a measuring device according to an embodiment of the present application;

FIG. 5 is a schematic diagram of data distribution of a training data set divided into two second types of training data subsets according to an embodiment of the present application; FIG.

FIG. 6 is a schematic diagram of obtaining a corresponding blood pressure prediction model according to a second type of training data subset division result according to an embodiment of the present application.

detailed description

Embodiments of the present application will be described below with reference to the accompanying drawings.

It should be understood that the measuring device mentioned in the embodiment of the present application generally refers to a wearable device. Specifically, the wearable device may be a watch, a wristband, etc., as shown in FIG. 1 , a watch with a blood pressure measuring function. .

FIG. 2 is a schematic diagram showing the basic configuration of a measuring device, which includes: a collector 210, a memory 220, a processor 230, a power source 240, and the like.

In one possible design, the measurement device may further include: a display unit 250, a Bluetooth module 260, and the like.

The measuring device mentioned in the embodiment of the present application is basically the same as the measuring device shown in FIG. 2 above. It should be noted that the embodiment of the present application does not relate to the improvement of the hardware configuration of the measuring device.

As shown in FIG. 3, the embodiment of the present application provides a blood pressure measurement method to solve the problem that the blood pressure measurement accuracy is not high, and the method includes:

Step 300: The measuring device extracts feature data in the biosignal according to the collected biometric signal of the user.

The biosignal here may be an electrocardiographic signal, or a pulse wave signal or the like.

The feature data extracted from the biosignal may be characteristic data capable of characterizing the biosignal such as the interval between the peak troughs and the full width at half maximum of the crest. The general method of extracting the feature data in the biosignal is to first extract the position of the feature point in the biosignal, such as the peak, trough and maximum slope point of the pulse wave signal, and then obtain the feature data based on the position of the feature point and the biosignal. . For example, the feature data extracted from the biosignal can be written as x0, x1, x2, ..., xn.

Step 310: The measuring device calculates the classification reference value according to the feature data and the preset classification parameter.

The preset classification parameter here can be A, which is denoted as a0, a1, a2, ..., an. For example, when the feature data is x0, x1, x2, ..., xn, the classification reference value calculated by the measuring device is a0*x0+a1*x1+a2*x2+...+an*xn.

Step 320: The measuring device determines the target blood pressure prediction model from the blood pressure prediction model set according to the preset threshold range in which the classification reference value falls.

Each blood pressure preset model corresponds to a preset threshold range, and the preset threshold range corresponding to the target blood pressure prediction model is the same as the preset threshold range in which the classification reference value falls.

For example, if the preset threshold range includes two, respectively, the classification reference value is greater than or equal to 0 and the classification reference value is less than 0, the blood pressure prediction model set also includes two, respectively, the corresponding classification reference value is greater than or equal to 0 and the classification reference value is less than 0. . When the classification reference value calculated in step 310 is greater than or equal to 0, the measuring device uses the blood pressure prediction model whose classification reference value is greater than or equal to 0 as the target blood pressure prediction model. When the classification reference value calculated in step 310 is less than 0, the measuring device will The blood pressure prediction model whose classification reference value is smaller than 0 is used as the target blood pressure prediction model.

It should be understood that when the preset threshold range includes two or more, two or more preset classification parameters are needed, and the two or more classification reference values that need to be calculated are combined to respectively correspond to different preset threshold ranges.

Step 330: The measuring device inputs the feature data into the target blood pressure prediction model to obtain the blood pressure measurement result of the user.

Therefore, through the above steps 300 to 320, the measuring device determines the target blood pressure prediction model, that is, the blood pressure preset model applicable to the current feature data, and then inputs the feature data to the target blood pressure prediction model.

It should be noted that each blood pressure prediction model in the blood pressure prediction model set has the characteristic data as an independent variable and the blood pressure value as a dependent variable.

As can be seen from the above, as shown in FIG. 4, the measuring device collects the biosignal of the user, extracts the feature data from the biosignal, calculates the classification reference value according to the preset classification parameter and the feature data extracted from the biosignal, and determines according to the classification reference value. The target blood pressure prediction model inputs the characteristic data to the target blood pressure prediction model to obtain a blood pressure measurement result. When the preset threshold range in which the classification reference value falls is different, the target blood pressure prediction model determined by the measuring device is also different. Therefore, the blood pressure measurement method provided by the embodiment of the present application can better adapt to the scene of blood pressure diversity, and is no longer directly The trait data is input into a single blood pressure prediction model, which effectively improves the accuracy of blood pressure measurement.

It should be understood that the preset classification parameters mentioned in step 310 and the blood pressure prediction model set mentioned in step 320 are obtained before the measurement device performs blood pressure measurement, and are saved in the measurement device.

The preset classification parameter and the blood pressure prediction model set may be obtained by establishing a classification model and a blood pressure prediction model, and may be, but not limited to, the following possible implementation manners.

First, the measurement device acquires a training data set.

Wherein, each training data in the training data set includes a characteristic data and an actual blood pressure measurement result, and the actual blood pressure measurement result includes systolic blood pressure and diastolic blood pressure, and the biosignal corresponding to the characteristic data in each training data The biosignal corresponding to the blood pressure measurement is the same.

It should be understood that the training data set includes a large amount of training data, the feature data in the kth training data is the feature data extracted for the kth biological signal, and the actual blood pressure measurement result in the kth training data is for the first The blood pressure measurement result of k biosignals, the actual blood pressure measurement result herein may refer to a blood pressure measurement result measured by a user using a tool such as a mercury sphygmomanometer or a cuff sphygmomanometer, and k is a positive integer.

Then, the measuring device establishes a classification model according to the training data set, and obtains a preset classification parameter. Specifically, the measuring device divides the training data set into at least two first types of training data subsets according to the actual blood pressure measurement results included in each training data, according to at least two first types of training data subsets and preset classifications. Function to get the preset classification parameters. Wherein, each of the first type of training data subsets corresponds to a range of values of the first type of actual blood pressure measurement results.

For example, the systolic blood pressure (SBP) is equal to 140, and the training data set is divided into two first training data subsets, and the first first training data subset includes the training data with an SBP less than 140. The training data, the second first type of training data subset includes training data with an SBP greater than or equal to 140 in the training data. The training data with the SBP less than 140 in the training data is marked as -1, and the training data with the SBP greater than or equal to 140 in the training data is marked as +1, and the preset classification parameter A is obtained by the preset classification function, and is recorded as a0, a1. , a2,...,an.

It should be understood that the above training data set is divided into two first types of training data subsets with SBP equal to 140, wherein the first first type training data subset represents a non-high voltage subset, and the second The first type of training data subset represents a high-voltage subset, which is only an example, and other classifications may be performed according to actual conditions. For example, the SBP is equal to 100, and the training data set is divided into two first training data subsets, and the first first training data subset includes training data with SBP less than 100 in the training data, indicating a low voltage subset. The second first type of training data subset includes training data in which the SBP is greater than or equal to 100 in the training data, indicating a non-low voltage subset. Of course, the training data set can also be divided into any two subsets of the first type of training data by using SBP equal to 110, 120, etc., regardless of whether there is a specific meaning such as high voltage and low voltage.

Next, the measuring device establishes a blood pressure prediction model according to each of the first type of training data subsets, and obtains a blood pressure prediction model set.

For example, the measuring device divides the training data set into two first training data subsets with the SBP equal to 140, and the preset classification parameters have been obtained according to the two first training data subsets and the preset classification function. Further, the measuring device obtains two blood pressure prediction models according to the two first training data subsets by using a preset regression function, which are respectively recorded as Model 1 and Model 2, for example, the regression function may be a linear regression function, wherein the model The parameter B of 1, denoted as b0, b1, b2, ..., bn, the classification reference value corresponding to model 1 is less than 0, the parameter C of model 2, denoted as c0, c1, c2, ..., cn, the classification corresponding to model 2 The reference value is greater than or equal to zero.

Further, assuming that the feature data is x0, x1, x2, ..., xn, when the classification reference value calculated by the measuring device is less than 0, the measuring device uses the model 1 as the target blood pressure prediction model, and obtains the blood pressure measurement result of the user as b0* X0+b1*x1+b2*x2+...+bn*xn; when the classification reference value calculated by the measuring device is greater than or equal to 0, the measuring device uses model 2 as the target blood pressure prediction model, and obtains the user's blood pressure measurement result as c0*x0. +c1*x1+c2*x2+...+cn*xn.

It should be understood that the embodiment is exemplified by systolic blood pressure, and the manner of predicting diastolic blood pressure is similar to the method of predicting systolic blood pressure, except that the dependent variable when the model is established is changed from systolic blood pressure to diastolic blood pressure.

Optionally, obtaining the blood pressure prediction model set may also adopt the following scheme:

The measuring device divides the training data set into at least two second types of training data subsets according to actual blood pressure measurement results included in each training data, and establishes a blood pressure prediction model according to each second type training data subset to obtain blood. A set of pressure prediction models. Wherein, each second type of training data subset corresponds to a second type of actual blood pressure measurement result value range.

Optionally, the number of the first type of training data subset is the same as the number of the second type training data subset, and the i-th first type training data subset corresponds to the ith first type of actual blood pressure measurement result range. Is a subset of the range of values of the i-th second type of actual blood pressure measurement corresponding to the i-th second-type training data subset, where i is a positive integer.

For example, the measuring device divides the training data set into two second types of training data subsets according to actual blood pressure measurement results included in each training data, as shown in FIG. 5, the first second type training data subset includes The training data has an SBP less than 150 training data, and the second second type training data subset includes training data with an SBP greater than or equal to 130 in the training data, and then the measuring device presets according to the two second types of training data subsets. The regression function obtains two blood pressure prediction models, which are model x and model y, respectively, as shown in Fig. 6. Among them, model x corresponds to the first first type of training data subset, and model x parameter B is recorded as b0, b1. , b2, ..., bn, model x also corresponds to the classification reference value is less than 0, model y corresponds to the second first type of training data subset, model y parameter C, denoted as c0, c1, c2, ..., cn, model y also corresponds to the classification reference value is greater than or equal to 0.

In the above embodiment, the training data set is divided into at least two second types of training data subsets, and the reason for not using the at least two first type training data subsets is that the different blood pressure prediction models are used. There is a certain degree of redundancy between the training data subsets to ensure the accuracy of blood pressure measurements near the classification nodes.

For another example, the training data set is divided into four first training data subsets by dividing SBP equal to 100 and SBP equal to 140. The first first training data subset includes training data with SBP less than 100 in the training data. The second first type training data subset includes training data in which the SBP is greater than or equal to 100 in the training data, and the third first type training data subset includes training data in which the SBP is less than 140 in the training data, and the fourth first type training The data subset includes training data with an SBP greater than or equal to 140 in the training data. Based on the first first type training data subset and the second first type training data subset, the preset classification parameter A1 is obtained through a preset classification function. And based on the third first type training data subset and the fourth first type training data subset, the preset classification parameter A2 is obtained by a preset classification function.

Further, the training data set is divided into three second types of training data subsets, and the first second type training data subset includes training data with an SBP less than 100 in the training data, and a second first training data subset. The training data includes training data with an SBP greater than or equal to 100 and an SBP less than 140, and the third first training data subset includes training data with an SBP greater than or equal to 140 in the training data, and the subset of the training data based on the three second types is passed. The preset regression function obtains three blood pressure prediction models, namely model x1, model x2, and model x3. In addition, when the training data set is divided into three second types of training data subsets, a certain degree of redundancy can also be set between the respective training data subsets.

Wherein, the model x1 corresponds to the classification reference value calculated according to A1 is less than 0, the model x2 corresponding to the classification reference value calculated according to A1 is greater than or equal to 0, and the classification reference value is less than 0 according to A2 calculation, and the model x3 correspondingly obtains the classification reference value according to A2. Equal to 0.

When the feature data is x0, x1, x2, ..., xn, the measuring device calculates the classification reference value according to A1, denoted as B1, and calculates the classification reference value according to A2, which is recorded as B2. When B1 is less than 0, the measuring device takes x1 as The target blood pressure prediction model, when B1 is greater than or equal to 0 and B2 is less than 0, the measuring device uses x2 as the target blood pressure prediction model, and when B3 is greater than or equal to 0, the measuring device uses x3 as the target blood pressure prediction model.

It should be understood that the foregoing process of obtaining the preset classification parameter and the blood pressure prediction model set may be performed by the measurement device or by other devices. After the other device performs the above process to obtain the preset classification parameter and the blood pressure prediction model set, The preset classification parameters and the blood pressure prediction model set are stored in the measurement device.

In addition, optionally, the measuring device calculates the classification reference value according to the feature data and the preset classification parameter, and the measuring device And determining, according to the preset threshold range in which the classification reference value falls, at least two target blood pressure prediction models, wherein the probability sum of the at least two target blood pressure prediction models is 1, and the measuring device inputs the feature data To each target blood pressure prediction model, the at least two target blood pressure prediction models are respectively obtained corresponding to blood pressure measurement results, and then the final user's blood pressure measurement results are determined according to the probability of each target blood pressure prediction model.

For example, the measuring device determines two target blood pressure prediction models from the blood pressure prediction model set according to the preset threshold range into which the classification reference value falls, respectively, the model 1 and the model 2, the probability of the model 1 is 70%, and the probability of the model 2 At 30%, the measuring device inputs the characteristic data to the two target blood pressure prediction models respectively, obtains two blood pressure measurement results, respectively S1 and S2, and then determines that the final user's blood pressure measurement result is 70%*S1+30%. *S2.

Based on the same concept, the present application further provides a blood pressure measuring device, which can be used to perform the method embodiment corresponding to FIG. 3 above. Therefore, the embodiment of the blood pressure measuring device provided by the embodiment of the present application can refer to the implementation manner of the method. , the repetition will not be repeated.

As shown in FIG. 2, the present application provides a blood pressure measuring device, wherein the collector 210 is configured to collect a biosignal of a user;

a memory 220, configured to store a computer program;

The processor 230 is configured to call a computer program stored in the memory to perform the following processing:

Extracting the feature data of the biometric signal according to the biosignal of the user collected by the collector 210; calculating the classification reference value according to the feature data and the preset classification parameter; and predicting the model from the blood pressure according to the preset threshold range in which the classification reference value falls Determining a target blood pressure prediction model in the collection, each blood pressure preset model corresponding to a preset threshold range, and the preset threshold range corresponding to the target blood pressure prediction model is the same as the preset threshold range in which the classification reference value falls; and then inputting the characteristic data into the target The blood pressure prediction model obtains the blood pressure measurement result of the user.

In a possible implementation, the processor 230 is further configured to: acquire a training data set, where each training data in the training data set includes one feature data and one actual blood pressure measurement result, and the feature data in each training data The corresponding biosignal is the same as the biosignal corresponding to the actual blood pressure measurement result; according to the actual blood pressure measurement result included in each training data, the training data set is divided into at least two first type training data subsets; according to at least two A type of training data subset and a preset classification function obtain preset classification parameters.

In a possible implementation, the processor 230 is further configured to: establish a blood pressure prediction model according to each of the first types of training data subsets, and obtain a blood pressure prediction model set.

In a possible implementation, the processor 230 is further configured to: divide the training data set into at least two second types of training data subsets according to actual blood pressure measurement results included in each training data, and the first type The number of training data subsets is the same as the number of the second type training data subsets; a blood pressure prediction model is established according to each second type training data subset to obtain a blood pressure prediction model set.

In a specific implementation process, the steps in the method of the embodiment corresponding to FIG. 3 may be completed by an integrated logic circuit of hardware in the processor 230 or an instruction in a form of software. The steps of the blood pressure measurement method disclosed in the embodiments of the present application may be directly implemented as hardware processor execution completion, or performed by a combination of hardware and software modules in the processor. Software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable Programmable memories, registers, etc. are well-established in the storage medium of the art. The storage medium is located in the memory 220, and the processor 230 reads the information in the memory 220, in conjunction with its hardware, to perform the steps in the method of the embodiment corresponding to FIG. To avoid repetition, it will not be described in detail here.

The present application also provides a computer readable storage medium having stored therein instructions that, when executed on a computer, cause the computer to perform the method of the embodiment corresponding to FIG. 3 described above.

In summary, the measuring device collects the user's biological signal, extracts the feature data from the biological signal, calculates the classification reference value according to the preset classification parameter and the feature data extracted from the biological signal, and determines the target blood pressure prediction model according to the classification reference value. When the preset threshold range in which the classification reference value falls is different, the target blood pressure prediction model determined by the measuring device is also different, and the characteristic data is input to the target blood pressure prediction model to obtain the blood pressure measurement result. Therefore, the blood pressure measurement method provided by the embodiment of the present application can better adapt to the scene of blood pressure diversity, and no longer directly input the feature data into a single blood pressure prediction model, thereby effectively improving the accuracy of the blood pressure measurement, and the method is simple. ,flexible. In addition, there is a certain degree of redundancy between the subsets of training data used to establish different blood pressure prediction models to ensure the accuracy of blood pressure measurement near the classification nodes.

Those skilled in the art will appreciate that embodiments of the present application can be provided as a method, system, or computer program product. Therefore, the embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Moreover, embodiments of the present application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.

Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block of the flowchart illustrations and/or FIG. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine for the execution of instructions for execution by a processor of a computer or other programmable data processing device. Means for implementing the functions specified in one or more of the flow or in a block or blocks of the flow chart.

The computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device. The apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.

These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device. The instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

It is apparent that those skilled in the art can make various modifications and variations to the embodiments of the present application without departing from the spirit and scope of the application. Thus, it is intended that the present invention cover the modifications and variations of the embodiments of the present invention.

Claims

A blood pressure measuring method, comprising:

The measuring device extracts feature data in the biosignal according to the collected biometric signal of the user;

The measuring device calculates a classification reference value according to the feature data and a preset classification parameter;

The measuring device determines a target blood pressure prediction model from the blood pressure prediction model set according to the preset threshold range in which the classification reference value falls, each blood pressure preset model corresponding to a preset threshold range, and the target blood pressure prediction model corresponds to The preset threshold range is the same as the preset threshold range in which the classification reference value falls;

The measuring device inputs the feature data into the target blood pressure prediction model to obtain a blood pressure measurement result of the user.
The method of claim 1 wherein said predetermined classification parameter is obtained by:

The measuring device acquires a training data set, each training data in the training data set includes a feature data and an actual blood pressure measurement result, and the biosignal corresponding to the feature data in each training data corresponds to an actual blood pressure measurement result. The same biological signal;

The measuring device divides the training data set into at least two first types of training data subsets according to actual blood pressure measurement results included in each piece of training data;

The measuring device obtains a preset classification parameter according to the at least two first type training data subsets and a preset classification function.
The method of claim 2 wherein said set of blood pressure prediction models is obtained by:

The measuring device establishes a blood pressure prediction model according to each of the first type of training data subsets, and obtains a blood pressure prediction model set.
The method of claim 2 wherein said set of blood pressure prediction models is obtained by:

The measuring device divides the training data set into at least two second types of training data subsets according to actual blood pressure measurement results included in each training data, the number of the first type of training data subsets and the second type of training The number of data subsets is the same;

The measuring device establishes a blood pressure prediction model according to each of the second types of training data subsets, and obtains a blood pressure prediction model set.
The method according to claim 4, wherein the number of the first type of training data subset is the same as the number of the second type of training data subset, and each of the first type of training data subsets corresponds to a first type of actual blood pressure The range of measurement results, each subset of the second training data corresponds to a range of actual blood pressure measurement results of the second type, and the i-th first type of actual blood pressure measurement corresponding to the i-th first-class training data subset The value range is a subset of the range of values of the i-th second-type actual blood pressure measurement corresponding to the i-th second-type training data subset, where i is a positive integer.
A blood pressure measuring device, comprising: a collector, a memory and a processor, wherein:

The collector is configured to collect a biosignal of a user;

The memory for storing a computer program;

The processor is configured to: invoke a computer program stored in the memory, and execute: extracting feature data in the biosignal according to a biosignal of a user collected by the collector; and according to the feature data and a preset Classification Calculating a classification reference value; determining a target blood pressure prediction model from the blood pressure prediction model set according to the preset threshold range in which the classification reference value falls, each blood pressure preset model corresponding to a preset threshold range, the target blood pressure prediction The preset threshold range corresponding to the model is the same as the preset threshold range in which the classification reference value falls; the feature data is input into the target blood pressure prediction model to obtain the blood pressure measurement result of the user.
The device according to claim 6, wherein the processor is further configured to:

Obtaining a training data set, where each training data in the training data set includes one feature data and an actual blood pressure measurement result, and the biosignal corresponding to the feature data in each training data is the same as the biosignal corresponding to the actual blood pressure measurement result;

Dividing the training data set into at least two first types of training data subsets according to actual blood pressure measurement results included in each training data;

Predetermining classification parameters are obtained according to the at least two first type training data subsets and a preset classification function.
The device according to claim 7, wherein the processor is further configured to:

A blood pressure prediction model is established according to each of the first type of training data subsets, and a blood pressure prediction model set is obtained.
The device according to claim 8, wherein the processor is further configured to:

Dividing the training data set into at least two second types of training data subsets according to actual blood pressure measurement results included in each training data, the number of the first type of training data subsets and the second type of training data subsets The same number;

A blood pressure prediction model is established according to each of the second type of training data subsets, and a blood pressure prediction model set is obtained.
The apparatus according to claim 9, wherein the number of the first type of training data subsets is the same as the number of the second type of training data subsets, and each of the first type of training data subsets corresponds to a first type of actual blood pressure The range of measurement results, each subset of the second training data corresponds to a range of actual blood pressure measurement results of the second type, and the i-th first type of actual blood pressure measurement corresponding to the i-th first-class training data subset The value range is a subset of the range of values of the i-th second-type actual blood pressure measurement corresponding to the i-th second-type training data subset, where i is a positive integer.
A computer readable storage medium, wherein the computer readable storage medium stores instructions for causing a computer to perform the method of any one of claims 1 to 5 when the instructions are run on a computer .