CN114098689A

CN114098689A - Pulse wave analysis method and apparatus

Info

Publication number: CN114098689A
Application number: CN202111419300.XA
Authority: CN
Inventors: 朱娜
Original assignee: Shanghai Zhangmen Science and Technology Co Ltd
Current assignee: Shanghai Zhangmen Science and Technology Co Ltd
Priority date: 2021-11-26
Filing date: 2021-11-26
Publication date: 2022-03-01

Abstract

The embodiment of the application discloses a pulse wave analysis method and device. One embodiment of the pulse wave analysis method includes: acquiring a plurality of pulse waves acquired by a matrix sensor on a pulse feeling patch at a plurality of points on the front face of the wrist of a user, wherein when a wrist strap on the pulse feeling patch is wound on the wrist, the matrix sensor fixed on the wrist strap contacts the plurality of points on the front face of the wrist; generating a target pulse wave based on the plurality of pulse waves; and analyzing the target pulse wave to obtain the symptom information of the user. According to the embodiment, the pulse feeling patch is thinned by the matrix sensor, so that pulse wave acquisition points of the pulse feeling patch are increased, and the pulse searching speed and the pulse searching precision are improved.

Description

Pulse wave analysis method and apparatus

Technical Field

The embodiment of the application relates to the technical field of computers, in particular to a pulse wave analysis method and device.

Background

Since the position and depth of the pulse vary from person to person, pulse searching is a difficult point in pulse diagnosis. At present, the traditional Chinese medical doctors with rich experience are required to manually find the pulse. Specifically, the patient is required to queue to take his or her number and then visit the patient in turn. Such a pattern is time consuming for a medical examination institution like traditional Chinese medicine. In order to solve the problem of pulse feeling time consumption, a plurality of intelligent pulse feeling products appear on the market in succession. Although the intelligent pulse feeling products can accelerate the pulse feeling speed to a certain extent, the intelligent pulse feeling products need the professional skill of judging the pulse position of the user, have larger body size and are unstable in fixation, so that the pulse feeling is difficult.

Disclosure of Invention

The embodiment of the application provides a pulse wave analysis method and device.

In a first aspect, an embodiment of the present application provides a pulse wave analysis method, including: acquiring a plurality of pulse waves acquired by a matrix sensor on a pulse feeling patch at a plurality of points on the front face of the wrist of a user, wherein when a wrist strap on the pulse feeling patch is wound on the wrist, the matrix sensor fixed on the wrist strap contacts the plurality of points on the front face of the wrist; generating a target pulse wave based on the plurality of pulse waves; and analyzing the target pulse wave to obtain the symptom information of the user.

In some embodiments, generating the target pulse wave based on the plurality of pulse waves includes: and analyzing the waveforms of the multiple pulse waves to obtain a target pulse wave meeting a preset condition.

In some embodiments, analyzing the waveforms of the plurality of pulse waves to obtain a target pulse wave satisfying a preset condition includes: selecting at least one pulse wave meeting preset conditions from a plurality of pulse waves; and generating a target pulse wave based on the pulse waves meeting the preset conditions.

In some embodiments, generating the target pulse wave based on the pulse waves satisfying the preset condition includes: and denoising and amplifying the pulse waves meeting the preset conditions to obtain the target pulse waves.

In some embodiments, generating the target pulse wave based on the pulse waves satisfying the preset condition includes: and synthesizing a target pulse wave based on the pulse waves meeting the preset condition.

In some embodiments, the preset conditions include at least one of: the waveform has periodicity and a peak value larger than a preset threshold value.

In some embodiments, analyzing the target pulse wave to obtain symptom information of the user includes: extracting feature information of the target pulse wave, wherein the feature information comprises at least one of the following items: time domain characteristics, frequency domain characteristics, pulse rate characteristics; and inputting the characteristic information of the target pulse wave into a pre-trained symptom prediction model to obtain the symptom information of the user.

In some embodiments, the symptom prediction model is trained by: acquiring a training sample, wherein the training sample comprises characteristic information and a symptom label of a sample pulse wave; and training to obtain a symptom prediction model by taking the characteristic information of the sample pulse wave as input and taking the symptom label of the sample pulse wave as output.

In a second aspect, an embodiment of the present application provides a pulse wave analysis apparatus, including: an acquisition module configured to acquire a plurality of pulse waves acquired by the matrix sensor on the pulse patch at a plurality of points on the front face of the wrist of the user, wherein the matrix sensor fixed on the wristband contacts the plurality of points on the front face of the wrist when the wristband on the pulse patch is entangled at the wrist; a generation module configured to generate a target pulse wave based on the plurality of pulse waves; and the analysis module is configured to analyze the target pulse wave to obtain the symptom information of the user.

In some embodiments, the generating module comprises: and the analysis submodule is configured to analyze the waveforms of the plurality of pulse waves to obtain a target pulse wave meeting a preset condition.

In some embodiments, the analysis submodule comprises: a selecting unit configured to select at least one pulse wave satisfying a preset condition from a plurality of pulse waves; a generation unit configured to generate a target pulse wave based on the pulse waves satisfying a preset condition.

In some embodiments, the generating unit is further configured to: and denoising and amplifying the pulse waves meeting the preset conditions to obtain the target pulse waves.

In some embodiments, the generating unit is further configured to: and synthesizing a target pulse wave based on the pulse waves meeting the preset condition.

In some embodiments, the analysis module is further configured to: extracting feature information of the target pulse wave, wherein the feature information comprises at least one of the following items: time domain characteristics, frequency domain characteristics, pulse rate characteristics; and inputting the characteristic information of the target pulse wave into a pre-trained symptom prediction model to obtain the symptom information of the user.

In a third aspect, an embodiment of the present application provides a computer device, including: one or more processors; a storage device having one or more programs stored thereon; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method as described in any implementation of the first aspect.

In a fourth aspect, the present application provides a computer-readable medium, on which a computer program is stored, which, when executed by a processor, implements the method as described in any implementation manner of the first aspect.

According to the pulse wave analysis method provided by the embodiment of the application, the pulse feeling patch is thinned slightly through the matrix sensor, so that the pulse feeling patch is lighter and more convenient, the pulse wave acquisition points of the pulse feeling patch are increased, and the pulse searching speed and the pulse searching precision are improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:

FIG. 1 is a schematic view of a pulse feeling patch;

FIG. 2 is a flow diagram of some embodiments of a pulse wave analysis method according to the present application;

FIG. 3 is a schematic diagram of pulse waves acquired by a matrix sensor;

FIG. 4 is a flow chart of still further embodiments of pulse wave analysis methods according to the present application;

FIG. 5 is a flow chart of further embodiments of a pulse wave analysis method according to the present application;

FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing the computer device of an embodiment of the present application.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Fig. 1 shows a schematic view of a pulse feeling patch. As shown in fig. 1, the pulse feeling patch may include a wristband 1 and a matrix sensor 2. The matrix sensor 2 is fixed to the wristband 1. The wrist strap 1 can be wound on the wrist of the user, so that the matrix sensor 2 is in large-area contact with the front surface of the wrist, and simultaneously, a plurality of pulse waves of a plurality of points on the front surface of the wrist are collected, thereby increasing the probability of collecting the pulse waves at the radial artery. The matrix sensor 2 is typically a set of sensors consisting of a plurality of small pressure sensors deployed in some geometrical pattern. The array of matrix sensors 2 increases the acquisition dimension compared to a single sensor, facilitating the acquisition of more data. And, constitute matrix sensor 2 by a plurality of little pressure sensor, do matrix sensor 2 and make for a little thin for when gathering the pulse ripples, the pulse feeling paster is more firm, and is lighter.

With continued reference to fig. 2, a flow 200 of some embodiments of a pulse wave analysis method according to the present application is shown. The pulse wave analysis method comprises the following steps:

step 201, acquiring a plurality of pulse waves acquired by a matrix sensor on a pulse feeling patch at a plurality of points on the front surface of the wrist of a user.

In this embodiment, the executing body of the pulse wave analysis method may acquire a plurality of pulse waves acquired by the matrix sensor on the pulse feeling patch at a plurality of points on the front surface of the wrist of the user.

The pulse feeling patch can comprise a wrist strap and a matrix sensor. The matrix sensor is fixed on the wristband. The wrist strap can be wound on the wrist of a user, so that the matrix sensor is in large-area contact with the front surface of the wrist, a plurality of pulse waves of a plurality of points on the front surface of the wrist are collected simultaneously, and the probability of collecting the pulse waves at the radial artery is increased.

When the wrist strap is wound on the wrist of a user, one pressure sensor in the matrix sensor contacts a point on the front face of the wrist to collect a pulse wave. For ease of understanding, fig. 3 shows a schematic diagram of the pulse waves acquired by the matrix sensor. As shown in fig. 3, the horizontal axis of the coordinate axis is time, the vertical axis of the coordinate axis is pressure value, and a continuous curve is a pulse wave acquired by one pressure sensor in the matrix sensor.

Step 202, generating a target pulse wave based on the plurality of pulse waves.

In this embodiment, the executing body may generate the target pulse wave based on a plurality of pulse waves.

The target pulse wave may be one pulse wave selected from a plurality of pulse waves, or may be one pulse wave generated based on a part of pulse waves. Generally, the waveforms of a plurality of pulse waves may be analyzed to obtain a target pulse wave satisfying a preset condition. The target pulse wave satisfying the preset condition may be a pulse wave of excellent waveform. The more excellent the waveform of the pulse wave is, the higher the probability that it is a pulse wave at the radial artery is. The radial artery can be automatically positioned without manually confirming the position of the radial artery, and the accuracy of the acquired pulse wave is higher. Wherein the preset condition may include, but is not limited to, at least one of the following: the waveform has periodicity, peaks greater than a preset threshold, and so on. For example, the time domain and frequency domain characteristics of a plurality of pulse waves are analyzed, and one pulse wave with a large peak value and good periodicity is selected as the target pulse wave.

And step 203, analyzing the target pulse wave to obtain symptom information of the user.

In this embodiment, the executing entity may analyze the target pulse wave to obtain the symptom information of the user.

Generally, different pulse wave waveforms correspond to different symptom information. Therefore, corresponding symptom information can be obtained according to the waveform of the target pulse wave.

With further reference to fig. 4, shown is a flow chart 400 of still further embodiments of a pulse wave analysis method according to the present application. The pulse wave analysis method comprises the following steps:

step 401, acquiring a plurality of pulse waves collected by a matrix sensor on a pulse feeling patch at a plurality of points on the front surface of the wrist of a user.

In this embodiment, the specific operation of step 401 has been described in detail in step 201 in the embodiment shown in fig. 2, and is not described herein again.

Step 402, selecting at least one pulse wave satisfying a preset condition from a plurality of pulse waves.

In this embodiment, the executing subject of the pulse wave analysis method may select at least one pulse wave satisfying a preset condition from the plurality of pulse waves.

In some embodiments, the executing subject may select one pulse wave satisfying a preset condition from the plurality of pulse waves. For example, the pulse wave with the largest peak value and the best periodicity is selected. In some embodiments, the executing subject may select a plurality of pulse waves satisfying a preset condition from the plurality of pulse waves. For example, a pulse wave having a waveform periodicity with all peaks larger than a preset threshold is selected.

In step 403, a target pulse wave is generated based on the pulse waves satisfying the preset condition.

In this embodiment, the executing entity may generate the target pulse wave based on the pulse wave satisfying the preset condition.

In some embodiments, the executing body may perform denoising and amplification on the pulse wave meeting the preset condition to obtain the target pulse wave. Generally, in the case of selecting only one pulse wave satisfying the preset condition, the pulse wave may be denoised and amplified to make the waveform of the pulse wave smoother, and the pulse wave is the target pulse wave.

In some embodiments, the executing body may synthesize the target pulse wave based on the pulse waves satisfying the preset condition. In general, when a plurality of pulse waves satisfying a predetermined condition are selected, a waveform average value of the plurality of pulse waves may be taken to generate a target pulse wave. The target pulse wave has the advantages of low data noise and smooth waveform.

Step 404, analyzing the target pulse wave to obtain the symptom information of the user.

In this embodiment, the specific operation of step 404 has been described in detail in step 203 in the embodiment shown in fig. 2, and is not described herein again.

As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, the flow 400 of the pulse wave analysis method in the present embodiment highlights the pulse wave generation step. Therefore, the scheme described in this embodiment selects the pulse wave satisfying the preset condition to generate the target pulse wave, so that the waveform of the target pulse wave is more excellent.

With further reference to fig. 5, a flow 500 is shown that is another embodiment of a pulse wave analysis method according to the present application. The pulse wave analysis method comprises the following steps:

step 501, acquiring a plurality of pulse waves acquired by a matrix sensor on a pulse feeling patch at a plurality of points on the front surface of a wrist of a user.

Step 502, generating a target pulse wave based on the plurality of pulse waves.

In the present embodiment, the specific operations of steps 501-502 have been described in detail in step 201-202 in the embodiment shown in fig. 2, and are not described herein again.

Step 503, extracting the feature information of the target pulse wave.

In the present embodiment, the executing subject of the pulse wave analysis method may extract the feature information of the target pulse wave. The characteristic information may be used to characterize the characteristics of the pulse wave, including but not limited to: time domain features, frequency domain features, pulse rate features, and the like.

Step 504, inputting the characteristic information of the target pulse wave into a pre-trained symptom prediction model to obtain the symptom information of the user.

In this embodiment, the executing agent may input the feature information of the target pulse wave to a pre-trained symptom prediction model to obtain the symptom information of the user.

In general, the symptom prediction model may be obtained by supervised training using a machine learning method and a training sample, and may predict corresponding symptom information based on feature information of a pulse wave.

The symptom prediction model can be obtained by training through the following steps:

first, a large number of training samples are obtained.

The training sample can comprise characteristic information of sample pulse waves and symptom labels. After the sample pulse wave is obtained, time domain analysis (such as morphological analysis) can be performed on the sample pulse wave, feature quantities such as a pulse rate and the like can be calculated, frequency domain analysis can be performed, feature quantities can be extracted from multiple dimensions, and the like. Based on the big data, the sample pulse wave is labeled with the corresponding symptom.

Then, the characteristic information of the sample pulse wave is used as input, the symptom label of the sample pulse wave is used as output, and a symptom prediction model is obtained through training.

Typically, before training, the various parameters of the model may be initialized with some different small random number. The small random number is used for ensuring that the model does not enter a saturation state due to overlarge weight value, so that training fails, and the difference is used for ensuring that the model can be normally learned. Parameters of the model can be continuously adjusted and cut in the training process, and the model can be iterated for a plurality of rounds until convergence, so that the symptom prediction model for predicting symptom information can be obtained.

As can be seen from fig. 5, compared with the embodiment corresponding to fig. 2, the flow 500 of the pulse wave analysis method in the present embodiment highlights the pulse wave analysis step. Therefore, the scheme described in the embodiment utilizes the symptom prediction model to predict the symptom information, and the prediction efficiency and the prediction effect are improved.

Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing the computer devices of embodiments of the present application. The computer device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 601.

It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or electronic device. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present application may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an acquisition module, a generation module, and an analysis module. The names of these modules do not constitute a limitation on the module itself in this case, for example, the determination module may also be described as a "module that acquires a plurality of pulse waves acquired by a matrix sensor on a pulse patch at a plurality of points on the front face of the wrist of the user".

As another aspect, the present application also provides a computer-readable medium, which may be contained in the computer device described in the above embodiments; or may exist separately and not be incorporated into the computer device. The computer readable medium carries one or more programs which, when executed by the computing device, cause the computing device to: acquiring a plurality of pulse waves acquired by a matrix sensor on a pulse feeling patch at a plurality of points on the front face of the wrist of a user, wherein when a wrist strap on the pulse feeling patch is wound on the wrist, the matrix sensor fixed on the wrist strap contacts the plurality of points on the front face of the wrist; generating a target pulse wave based on the plurality of pulse waves; and analyzing the target pulse wave to obtain the symptom information of the user.

The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims

1. A pulse wave analysis method comprising:

acquiring a plurality of pulse waves acquired by a matrix sensor on a pulse feeling patch at a plurality of points on the front face of a wrist of a user, wherein the matrix sensor fixed on a wristband contacts the plurality of points on the front face of the wrist when the wristband on the pulse feeling patch is entangled at the wrist;

generating a target pulse wave based on the plurality of pulse waves;

and analyzing the target pulse wave to obtain the symptom information of the user.

2. The method of claim 1, wherein the generating a target pulse wave based on the plurality of pulse waves comprises:

and analyzing the waveforms of the pulse waves to obtain the target pulse waves meeting preset conditions.

3. The method according to claim 2, wherein the analyzing the waveforms of the plurality of pulse waves to obtain the target pulse wave satisfying a preset condition comprises:

selecting at least one pulse wave meeting the preset condition from the plurality of pulse waves;

and generating the target pulse wave based on the pulse waves meeting the preset conditions.

4. The method according to claim 3, wherein the generating the target pulse wave based on the pulse waves satisfying the preset condition includes:

and denoising and amplifying the pulse waves meeting the preset conditions to obtain the target pulse waves.

5. The method according to claim 3, wherein the generating the target pulse wave based on the pulse waves satisfying the preset condition includes:

and synthesizing the target pulse wave based on the pulse waves meeting the preset condition.

6. The method according to any one of claims 2-5, wherein the preset conditions include at least one of: the waveform has periodicity and a peak value larger than a preset threshold value.

7. The method of claim 1, wherein the analyzing the target pulse wave for symptom information of the user comprises:

extracting feature information of the target pulse wave, wherein the feature information includes at least one of: time domain characteristics, frequency domain characteristics, pulse rate characteristics;

and inputting the characteristic information of the target pulse wave into a pre-trained symptom prediction model to obtain the symptom information of the user.

8. The method of claim 7, wherein the symptom prediction model is trained by:

acquiring a training sample, wherein the training sample comprises characteristic information and a symptom label of a sample pulse wave;

and training to obtain the symptom prediction model by taking the characteristic information of the sample pulse wave as input and taking the symptom label of the sample pulse wave as output.

9. A computer device, comprising:

one or more processors;

a storage device on which one or more programs are stored;

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-8.