CN117390406B

CN117390406B - Electrocardiogram monitoring method and system based on block chain

Info

Publication number: CN117390406B
Application number: CN202311706908.XA
Authority: CN
Inventors: 李峰
Original assignee: Shenzhen Dawei Medical Technology Development Co ltd
Current assignee: Shenzhen Dawei Medical Technology Development Co ltd
Priority date: 2023-12-13
Filing date: 2023-12-13
Publication date: 2024-04-09
Anticipated expiration: 2043-12-13
Also published as: CN117390406A

Abstract

The invention relates to an electrocardiosignal monitoring method and system based on a blockchain, wherein the method comprises the following steps that S1, after original electrocardiosignals of each edge device are read, the original electrocardiosignals of target edge devices are detected for the first time, namely sampling samples are randomly selected, and QRS waves are extracted through wavelet transformation; s2, judging that the obtained electrocardiosignals after wavelet transformation are the ratio of QRS wave groups, if the ratio of the QRS wave groups corresponding to the target edge equipment is lower than a first threshold value, marking the target edge equipment as invalid equipment, and discarding all original electrocardiosignals corresponding to the invalid equipment; and if the ratio of the QRS complex corresponding to the target edge equipment is not lower than a first threshold value, re-reading the original electrocardiosignal of the target edge equipment, denoising, S3, taking the target edge equipment data as node data of a blockchain to be uplink, S4, carrying out QRS wave extraction detection, and judging whether the signal is the QRS complex, and then carrying out signal preprocessing and electrocardiographic analysis monitoring.

Description

Electrocardiogram monitoring method and system based on block chain

Technical Field

The invention belongs to the field of data processing, and particularly relates to an electrocardio monitoring method and system based on a block chain.

Background

In the prior art, in order to perform electrocardiographic monitoring on edge equipment of a mobile terminal, there is a technology of processing electrocardiographic signal data through a blockchain, for example, in the prior art, denoising processing is performed after original electrocardiographic signals are read first; simultaneously taking each edge device as a node of a block chain, and uploading data;

then, QRS wave extraction and detection are carried out, QRS waves are extracted through wavelet transformation, and whether the signal is a QRS wave group is judged after the electrocardiosignal processed through wavelet transformation is obtained;

signal preprocessing, namely detecting the position of an R peak by the QRS wave if the wave crest is the QRS wave, taking the position of the R peak as a datum point, taking sampling points forwards and backwards as a heart beat, and dividing the heart beat, so as to obtain a plurality of heart beats; if the wave crest is a non-QRS wave, carrying out packet loss treatment;

however, the data error rate of the edge device of the mobile terminal is very high in the prior art, so that the existing technology for processing the electrocardiosignal data through the blockchain firstly reads the original electrocardiosignal and then performs denoising processing, so that a lot of computing resources are wasted, the data is uplink, then QRS wave extraction detection is performed, whether the signal is a QRS complex or not is judged, a lot of computing resources of the blockchain server are wasted, the computing resources of the blockchain server are wasted due to the fact that the data packet loss processing with high probability is also required in the subsequent signal preprocessing, and in a word, the existing technology for processing the electrocardiosignal data through the blockchain is very high in the ratio of the computing resources is wasted, so that the efficiency and the efficiency are poor.

Disclosure of Invention

The invention aims to provide an electrocardio monitoring method and system based on a block chain, which are used for solving the problems in the background technology.

In order to solve the technical problems, the invention provides the following technical scheme:

the block chain-based electrocardiosignal monitoring method comprises the steps of S1, after original electrocardiosignals of each edge device are read, performing first detection on the original electrocardiosignals of target edge devices, namely randomly selecting sampling samples, and extracting QRS waves through wavelet transformation; s2, judging that the obtained electrocardiosignals after wavelet transformation are the ratio of QRS wave groups, if the ratio of the QRS wave groups corresponding to the target edge equipment is lower than a first threshold value, marking the target edge equipment as invalid equipment, and discarding all original electrocardiosignals corresponding to the invalid equipment; and if the ratio of the QRS complex corresponding to the target edge equipment is not lower than a first threshold value, re-reading the original electrocardiosignal of the target edge equipment, denoising, S3, taking the target edge equipment data as node data of a blockchain to be uplink, S4, carrying out QRS wave extraction detection, and judging whether the signal is the QRS complex, and then carrying out signal preprocessing and electrocardiographic analysis monitoring.

Further, the QRS wave extraction process by wavelet transformation includes the steps of: data preprocessing: including filtering and de-baselining drift; wavelet transformation: decomposing the electrocardiosignal into components with different frequencies and times; QRS wave detection: determining the R wave position by searching zero crossing points between wavelet transformation mode maximum value pairs, and then searching local mode maximum value pairs behind the R wave front to determine Q waves and S waves; feature extraction: features of the QRS wave, such as peak and width, are extracted for calculating heart rate.

Further, the method for judging whether the electrocardiosignal of a certain section is a QRS complex comprises the following steps: the difference method is as follows: determining the existence and position of the QRS wave by carrying out first-order or second-order difference on the electrocardiosignal and judging whether the difference value exceeds a specific threshold value; QRS complex width detection: the electrocardiosignal is filtered by a digital filter, so that a pulse signal is output when the QRS complex arrives, the output pulse amplitude is compared with a threshold value, the QRS complex can be detected, and the width parameter is determined.

Further, the process of denoising the electrocardiosignal comprises the following steps: data preprocessing: including filtering and de-baselining drift; wavelet transformation: carrying out multistage wavelet decomposition on the preprocessed electrocardiosignals to obtain a series of wavelet coefficients; threshold denoising: selecting a threshold value to denoise the signal by utilizing the wavelet coefficient; the threshold selection method comprises a hard threshold and a soft threshold; the hard threshold sets the coefficients below the threshold to zero, and the coefficients above the threshold remain; the soft threshold value sets the coefficient smaller than the threshold value to zero, and the coefficient larger than the threshold value is contracted; reconstructing the signal: and carrying out inverse transformation on the denoised wavelet coefficient to obtain denoised electrocardiosignal.

Further, the specific process of the target edge device data as the node data of the blockchain is as follows: acquiring and processing original data of target edge equipment; transmitting the processed data to a cloud platform for storage; carrying out hash calculation on the processed target edge equipment data to obtain a target edge equipment hash value, and packaging the target edge equipment hash value into a target edge equipment transaction packet; signing the target edge device transaction package by using a private key in a trusted execution environment, wherein the private key is stored in the trusted execution environment in an unreadable mode; and combining the signed target edge device transaction package with the public key and then sending the combined target edge device transaction package to the blockchain cluster.

The electrocardio monitoring system based on the block chain comprises a signal pre-detection module, a signal discrimination module, a data uplink module and a data monitoring module; the signal pre-detection module is used for carrying out first detection on the original electrocardiosignals of the target edge equipment after the original electrocardiosignals of each edge equipment are read, namely randomly selecting sampling samples and extracting QRS waves through wavelet transformation; the signal discrimination module is used for judging that the obtained electrocardiosignals after wavelet transformation are the ratio of QRS wave groups, if the ratio of the QRS wave groups corresponding to the target edge equipment is lower than a first threshold value, marking the target edge equipment as invalid equipment, and discarding all the original electrocardiosignals corresponding to the invalid equipment; if the ratio of the QRS complex corresponding to the target edge equipment is not lower than a first threshold value, re-reading the original electrocardiosignal of the target edge equipment and denoising; the data uplink module is used for taking the target edge equipment data as node data uplink of the block chain;

the data monitoring module is used for carrying out QRS wave extraction detection, judging whether the signal is the QRS wave group, and then carrying out signal preprocessing and electrocardiographic analysis monitoring.

The beneficial effects are that: according to the method and the device, whether the ratio of the QRS complex corresponding to the target edge device is lower than the first threshold value is judged, the target edge device which is lower than the first threshold value is marked as invalid device, and all original electrocardiosignals corresponding to the invalid device are discarded, so that screening of the target device is achieved, the data corresponding to the invalid device does not need to be calculated by wasting calculation resources, and efficiency is improved.

Drawings

FIG. 1 is a flow chart of the method of the present invention.

Detailed Description

The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The application discloses an electrocardio monitoring method based on a blockchain, which comprises the following steps of S1, after original electrocardiosignals of each edge device are read, carrying out first detection on the original electrocardiosignals of target edge devices, namely randomly selecting sampling samples, and extracting QRS waves through wavelet transformation; the process of extracting the QRS wave by the wavelet transformation of S1 comprises the following steps: 1. data preprocessing: including filtering and de-baselining drift. 2. Wavelet transformation: the electrocardiographic signals are decomposed into components of different frequencies and times. Qrs wave detection: and determining the R wave position by searching zero crossing points between wavelet transformation mode maximum value pairs, and then searching local mode maximum value pairs behind the R wave front to determine Q waves and S waves. 4. Feature extraction: extracting features of the QRS wave, such as peak and width, for calculating heart rate; s2 judging the ratio of the electrocardiosignals to the QRS complex after the wavelet transformation processing is obtained, wherein the method for judging whether the electrocardiosignals at a certain section are the QRS complex or not by S2 comprises the following steps: 1. the difference method is as follows: the presence and position of the QRS wave are determined by carrying out first-order or second-order difference on the electrocardiosignal and judging whether the difference value exceeds a specific threshold value. The method is a rapid algorithm and is suitable for the electrocardiograph automatic monitor with high real-time requirements. 2. Threshold method: the detection of the R peak value point is a primary problem in QRS complex detection, and other details of the electrocardiographic signal can be analyzed only if the position of the R peak is determined. Common methods for detecting R peaks include an amplitude method, an area method and a slope method. Amplitude and area methods are prone to error when high T-waves occur in the electrocardiographic signals, and slope methods are superior to amplitude and area methods in this respect. Qrs complex width detection: the electrocardiosignal is filtered by a digital filter, so that a pulse signal is output when the QRS wave group arrives, and the amplitude of the pulse reflects the comprehensive characteristic information of the amplitude and the frequency of the QRS wave group to a certain extent. The QRS complex can be detected by comparing the output pulse amplitude with a certain threshold value, and the width parameter is determined, so that the method is also called a threshold value method. If the ratio of the QRS complex corresponding to the target edge device is lower than a first threshold value, marking the target edge device as invalid device, and discarding all original electrocardiosignals corresponding to the invalid device;

if the ratio of the QRS complex corresponding to the target edge equipment is not lower than a first threshold value, re-reading the original electrocardiosignal of the target edge equipment, and carrying out denoising processing on the electrocardiosignal, wherein the denoising processing process of the electrocardiosignal comprises the following steps of: 1. data preprocessing: including filtering and de-baselining drift. The purpose of the filtering is to remove high frequency noise, such as electromagnetic interference, and low frequency noise, such as mains interference. The purpose of the de-baselined drift is to remove the dc component of the signal to avoid its impact on the wavelet transform process. 2. Wavelet transformation: and carrying out multistage wavelet decomposition on the preprocessed electrocardiosignal to obtain a series of wavelet coefficients. These coefficients reflect the characteristics of the signal at different frequencies and times. 3. Threshold denoising: and selecting a proper threshold value to denoise the signal by utilizing the wavelet coefficient. The choice of threshold value may be determined based on empirical or experimental data. Common threshold selection methods are hard and soft thresholds. The hard threshold sets the coefficients below the threshold to zero, and the coefficients above the threshold remain; the soft threshold sets coefficients below the threshold to zero and coefficients above the threshold shrink to some extent. 4. Reconstructing the signal: and carrying out inverse transformation on the denoised wavelet coefficient to obtain denoised electrocardiosignal. It is noted that the purpose of the denoising process is to remove noise as much as possible while preserving the characteristics of the electrocardiographic signal, such as QRS complex, etc. Therefore, these two factors need to be balanced when selecting the threshold to obtain the best denoising effect.

S3, the target edge device data is used as the node data uplink of the block chain, and the specific process of the S3, the target edge device data is used as the node data uplink of the block chain, is as follows: 1. and acquiring and processing the original data of the target edge equipment. 2. And transmitting the processed data to a cloud platform for storage. 3. And carrying out hash calculation on the processed target edge equipment data to obtain a target edge equipment hash value, and packaging the target edge equipment hash value into a target edge equipment transaction packet. 4. The target edge device transaction package is signed in the trusted execution environment using a private key that is stored in the trusted execution environment in an unreadable manner. 5. And combining the signed target edge device transaction package with the public key and then sending the combined target edge device transaction package to the blockchain cluster. And S4, carrying out QRS wave extraction detection, judging whether the signal is a QRS wave group, and then carrying out signal preprocessing and electrocardiographic analysis monitoring.

The specific process of signal preprocessing and electrocardiograph analysis monitoring comprises the following steps: ST segment analysis: in signal preprocessing and electrocardiographic monitoring, the ST segment is a calm segment located between the QRS wave and the T wave. The ST segment may reflect myocardial ischemia or injury. Heart rate calculation: the heart rate is calculated by calculating the time interval between adjacent QRS waves. Heart rate is an important measure of the frequency of beating the heart. Rhythm analysis: the rhythm of the heart is analyzed by observing the waveform sequence in the electrocardiogram. For example, a normal heart rhythm is regular, while an arrhythmia may be irregular.

According to the method and the device, whether the ratio of the QRS complex corresponding to the target edge device is lower than the first threshold value is judged, the target edge device which is lower than the first threshold value is marked as invalid device, and all original electrocardiosignals corresponding to the invalid device are discarded, so that screening of the target device is achieved, the data corresponding to the invalid device does not need to be calculated by wasting calculation resources, and the efficiency is improved.

Embodiments of the present application that require protection include:

Preferably, the QRS wave extraction process by wavelet transformation comprises the steps of: data preprocessing: including filtering and de-baselining drift; wavelet transformation: decomposing the electrocardiosignal into components with different frequencies and times; QRS wave detection: determining the R wave position by searching zero crossing points between wavelet transformation mode maximum value pairs, and then searching local mode maximum value pairs behind the R wave front to determine Q waves and S waves; feature extraction: features of the QRS wave, such as peak and width, are extracted for calculating heart rate.

Preferably, the method for judging whether the electrocardiosignal of a certain section is a QRS complex comprises the following steps: the difference method is as follows: determining the existence and position of the QRS wave by carrying out first-order or second-order difference on the electrocardiosignal and judging whether the difference value exceeds a specific threshold value; QRS complex width detection: the electrocardiosignal is filtered by a digital filter, so that a pulse signal is output when the QRS complex arrives, the output pulse amplitude is compared with a threshold value, the QRS complex can be detected, and the width parameter is determined.

Preferably, the process of denoising the electrocardiosignal comprises the following steps: data preprocessing: including filtering and de-baselining drift; wavelet transformation: carrying out multistage wavelet decomposition on the preprocessed electrocardiosignals to obtain a series of wavelet coefficients; threshold denoising: selecting a threshold value to denoise the signal by utilizing the wavelet coefficient; the threshold selection method comprises a hard threshold and a soft threshold; the hard threshold sets the coefficients below the threshold to zero, and the coefficients above the threshold remain; the soft threshold value sets the coefficient smaller than the threshold value to zero, and the coefficient larger than the threshold value is contracted; reconstructing the signal: and carrying out inverse transformation on the denoised wavelet coefficient to obtain denoised electrocardiosignal.

Preferably, the specific process of the target edge device data being the node data of the blockchain is as follows: acquiring and processing original data of target edge equipment; transmitting the processed data to a cloud platform for storage; carrying out hash calculation on the processed target edge equipment data to obtain a target edge equipment hash value, and packaging the target edge equipment hash value into a target edge equipment transaction packet; signing the target edge device transaction package by using a private key in a trusted execution environment, wherein the private key is stored in the trusted execution environment in an unreadable mode; and combining the signed target edge device transaction package with the public key and then sending the combined target edge device transaction package to the blockchain cluster.

The electrocardio monitoring system based on the block chain comprises a signal pre-detection module, a signal discrimination module, a data uplink module and a data monitoring module; the signal pre-detection module is used for carrying out first detection on the original electrocardiosignals of the target edge equipment after the original electrocardiosignals of each edge equipment are read, namely randomly selecting sampling samples and extracting QRS waves through wavelet transformation; the signal discrimination module is used for judging that the obtained electrocardiosignals after wavelet transformation are the ratio of QRS wave groups, if the ratio of the QRS wave groups corresponding to the target edge equipment is lower than a first threshold value, marking the target edge equipment as invalid equipment, and discarding all the original electrocardiosignals corresponding to the invalid equipment; if the ratio of the QRS complex corresponding to the target edge equipment is not lower than a first threshold value, re-reading the original electrocardiosignal of the target edge equipment and denoising;

the data uplink module is used for taking the target edge equipment data as node data uplink of the block chain;

the data monitoring module is used for carrying out QRS wave extraction detection, judging whether the signal is QRS wave group, and then carrying out signal preprocessing and electrocardio analysis monitoring.

The embodiment of the application also provides a computer device, which can comprise a terminal device or a server, and the data computing program of the block chain-based electrocardio monitoring method can be configured in the computer device. The computer device is described below.

If the computer device is a terminal device, the embodiment of the present application provides a terminal device, taking the terminal device as a mobile phone as an example:

the mobile phone comprises: radio Frequency (RF) circuitry, memory, input unit, display unit, sensors, audio circuitry, wireless fidelity (Wireless Fidelity, wiFi) module, processor, and power supply.

The RF circuit can be used for receiving and transmitting signals in the process of receiving and transmitting information or communication, particularly, after receiving downlink information of the base station, the downlink information is processed by the processor; in addition, the data of the design uplink is sent to the base station. Typically, RF circuitry includes, but is not limited to, antennas, at least one amplifier, transceivers, couplers, low noise amplifiers (Low NoiseAmplifier, LNA for short), diplexers, and the like. In addition, the RF circuitry may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to global system for mobile communications (Global System of Mobile communication, GSM for short), general packet radio service (GeneralPacket Radio Service, GPRS for short), code division multiple access (Code Division Multiple Access, CDMA for short), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA for short), long term evolution (Long Term Evolution, LTE for short), email, short message service (Short Messaging Service, SMS for short), and the like.

The memory may be used to store software programs and modules, and the processor executes the software programs and modules stored in the memory to perform various functional applications and data processing of the handset. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

The input unit may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the handset. In particular, the input unit may include a touch panel and other input devices. The touch panel, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations thereon or thereabout by a user using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a predetermined program. Alternatively, the touch panel may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor, and can receive and execute commands sent by the processor. In addition, the touch panel may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. The input unit may include other input devices in addition to the touch panel. In particular, other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, mouse, joystick, etc.

The display unit may be used to display information input by a user or information provided to the user and various menus of the mobile phone. The display unit may include a display panel, which may be optionally configured in the form of a liquid crystal display (LiquidCrystal Display, LCD) or an Organic Light-Emitting Diode (OLED) or the like. Further, the touch panel may overlay the display panel, and upon detection of a touch operation thereon or thereabout, the touch panel is transferred to the processor to determine the type of touch event, and the processor then provides a corresponding visual output on the display panel based on the type of touch event. Although in the figures the touch panel and the display panel are shown as two separate components to implement the input and output functions of the cell phone, in some embodiments the touch panel and the display panel may be integrated to implement the input and output functions of the cell phone.

The handset may also include at least one sensor, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel according to the brightness of ambient light, and the proximity sensor may turn off the display panel and/or the backlight when the mobile phone moves to the ear. As one of the motion sensors, the accelerometer sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and direction when stationary, and can be used for applications of recognizing the gesture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that may also be configured with the handset are not described in detail herein.

Audio circuitry, speakers, and microphone may provide an audio interface between the user and the handset. The audio circuit can transmit the received electric signal after the audio data conversion to a loudspeaker, and the loudspeaker converts the electric signal into a sound signal to be output; on the other hand, the microphone converts the collected sound signals into electrical signals, which are received by the audio circuit and converted into audio data, which are processed by the audio data output processor and sent via the RF circuit to, for example, another mobile phone, or which are output to a memory for further processing.

WiFi belongs to a short-distance wireless transmission technology, and a mobile phone can help a user to send and receive an email, browse a webpage, access streaming media and the like through a WiFi module, so that wireless broadband Internet access is provided for the user. Although a WiFi module is illustrated, it is understood that it does not belong to the necessary configuration of the mobile phone, and can be omitted entirely as needed within the scope of not changing the essence of the invention.

The processor is a control center of the mobile phone, and is connected with various parts of the whole mobile phone by various interfaces and lines, and executes various functions and processes data of the mobile phone by running or executing software programs and/or modules stored in the memory and calling data stored in the memory, so that the mobile phone is monitored integrally. In the alternative, the processor may include one or more processing units; preferably, the processor may integrate an application processor and a modem processor, wherein the application processor primarily handles operating systems, user interfaces, applications, etc., and the modem processor primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor.

The handset further includes a power source (e.g., a battery) for powering the various components, preferably in logical communication with the processor through a power management system, such that functions such as managing charge, discharge, and power consumption are performed by the power management system.

Although not shown, the mobile phone may further include a camera, a bluetooth module, etc., which will not be described herein.

In this embodiment, the processor included in the terminal device further has the following functions:

a data calculation program of the blockchain-based electrocardiographic monitoring method is executed.

If the computer device is a server, the embodiments of the present application further provide a server, where the server may generate a relatively large difference due to different configurations or performances, and may include one or more central processing units (Central Processing Units, abbreviated as CPUs) (e.g., one or more processors) and a memory, one or more storage media (e.g., one or more mass storage devices) storing application programs or data. The memory and storage medium may be transitory or persistent. The program stored on the storage medium may include one or more modules, each of which may include a series of instruction operations on the server. Still further, the central processor may be configured to communicate with a storage medium and execute a series of instruction operations on the storage medium on a server.

The server may also include one or more power supplies, one or more wired or wireless network interfaces, one or more input/output interfaces, and/or one or more operating systems, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, etc.

In addition, the embodiment of the application also provides a storage medium for storing a computer program for executing the method provided by the embodiment.

The present embodiments also provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method provided by the above embodiments.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, where the above program may be stored in a computer readable storage medium, and when the program is executed, the program performs steps including the above method embodiments; and the aforementioned storage medium may be at least one of the following media: read-only Memory (ROM), RAM, magnetic disk or optical disk, etc.

The present application also discloses a blockchain-based electrocardiographic monitoring system that includes a computer program product of instructions that, when executed on a computer, cause the computer to perform the blockchain-based electrocardiographic monitoring method described above.

It should be noted that, in the present specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment is mainly described in a different point from other embodiments. In particular, for the apparatus and system embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, with reference to the description of the method embodiments in part. The apparatus and system embodiments described above are merely illustrative, in which elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

The foregoing is merely one specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. The electrocardio monitoring method based on the block chain is characterized by comprising the following steps of S1: after the original electrocardiosignals of each edge device are read, the original electrocardiosignals of the target edge device are detected for the first time, namely, sampling samples are randomly selected and the QRS wave is extracted through wavelet transformation; s2: judging that the obtained electrocardiosignals after wavelet transformation are the ratio of QRS wave groups, if the ratio of the QRS wave groups corresponding to the target edge equipment is lower than a first threshold value, marking the target edge equipment as invalid equipment, and discarding all the original electrocardiosignals corresponding to the invalid equipment; if the ratio of the QRS complex corresponding to the target edge equipment is not lower than a first threshold value, re-reading the original electrocardiosignal of the target edge equipment and denoising; s3: then, the target edge equipment data is used as node data of the block chain to be uplink; s4: and then, carrying out QRS wave extraction detection, judging whether the signal is a QRS wave group, and then, carrying out signal preprocessing and electrocardiographic analysis monitoring.

2. The blockchain-based electrocardiographic monitoring method according to claim 1, wherein the QRS wave extraction process by wavelet transformation includes the steps of: data preprocessing: including filtering and de-baselining drift; wavelet transformation: decomposing the electrocardiosignal into components with different frequencies and times; QRS wave detection: determining the R wave position by searching zero crossing points between wavelet transformation mode maximum value pairs, and then searching local mode maximum value pairs behind the R wave front to determine Q waves and S waves; feature extraction: features of the QRS wave, including peak and width, are extracted for calculating heart rate.

3. The blockchain-based electrocardiographic monitoring method of claim 1 wherein the method of determining whether a segment of the electrocardiographic signal is a QRS complex comprises: the difference method is as follows: determining the existence and position of the QRS wave by carrying out first-order or second-order difference on the electrocardiosignal and judging whether the difference value exceeds a specific threshold value; QRS complex width detection: the electrocardiosignal is filtered by a digital filter, so that a pulse signal is output when the QRS complex arrives, the output pulse amplitude is compared with a threshold value, the QRS complex can be detected, and the width parameter is determined.

4. The blockchain-based electrocardiographic monitoring method according to claim 1, wherein the process of denoising the electrocardiographic signal comprises the steps of: data preprocessing: including filtering and de-baselining drift; wavelet transformation: carrying out multistage wavelet decomposition on the preprocessed electrocardiosignals to obtain a series of wavelet coefficients; threshold denoising: selecting a threshold value to denoise the signal by utilizing the wavelet coefficient; the threshold selection method comprises a hard threshold and a soft threshold; the hard threshold sets the coefficients below the threshold to zero, and the coefficients above the threshold remain; the soft threshold value sets the coefficient smaller than the threshold value to zero, and the coefficient larger than the threshold value is contracted; reconstructing the signal: and carrying out inverse transformation on the denoised wavelet coefficient to obtain denoised electrocardiosignal.

5. The blockchain-based electrocardiographic monitoring method according to claim 1, wherein the specific process of using the target edge device data as the node data uplink of the blockchain is as follows: acquiring and processing original data of target edge equipment; transmitting the processed data to a cloud platform for storage; carrying out hash calculation on the processed target edge equipment data to obtain a target edge equipment hash value, and packaging the target edge equipment hash value into a target edge equipment transaction packet; signing the target edge device transaction package by using a private key in a trusted execution environment, wherein the private key is stored in the trusted execution environment in an unreadable mode; and combining the signed target edge device transaction package with the public key and then sending the combined target edge device transaction package to the blockchain cluster.

6. The electrocardio monitoring system based on the block chain is characterized by comprising a signal pre-detection module, a signal discrimination module, a data uplink module and a data monitoring module; the signal pre-detection module is used for carrying out first detection on the original electrocardiosignals of the target edge equipment after the original electrocardiosignals of each edge equipment are read, namely randomly selecting sampling samples and extracting QRS waves through wavelet transformation; the signal discrimination module is used for judging that the obtained electrocardiosignals after wavelet transformation are the ratio of QRS wave groups, if the ratio of the QRS wave groups corresponding to the target edge equipment is lower than a first threshold value, marking the target edge equipment as invalid equipment, and discarding all the original electrocardiosignals corresponding to the invalid equipment; if the ratio of the QRS complex corresponding to the target edge equipment is not lower than a first threshold value, re-reading the original electrocardiosignal of the target edge equipment and denoising; the data uplink module is used for taking the target edge equipment data as node data uplink of the block chain; the data monitoring module is used for carrying out QRS wave extraction detection, judging whether the signal is the QRS wave group, and then carrying out signal preprocessing and electrocardiographic analysis monitoring.