CN114027846B - Intelligent electrocardiosignal processing method - Google Patents

Intelligent electrocardiosignal processing method Download PDF

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CN114027846B
CN114027846B CN202111298084.8A CN202111298084A CN114027846B CN 114027846 B CN114027846 B CN 114027846B CN 202111298084 A CN202111298084 A CN 202111298084A CN 114027846 B CN114027846 B CN 114027846B
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electrocardiosignal
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CN114027846A (en
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唐聪能
刘哲
宋元林
杜春玲
吴超民
袁再鑫
粟锦平
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Hunan Ventmed Medical Technology Co Ltd
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Abstract

The invention relates to the technical field of signal processing, and discloses an intelligent electrocardiosignal processing method, which comprises the following steps: acquiring electrocardiosignals and preprocessing the electrocardiosignals; extracting the characteristics of the preprocessed electrocardiosignals to obtain electrocardiosignal characteristics; taking the electrocardiosignal characteristics as input and the electrocardiosignal classification result as output, constructing a deep stack network electrocardiosignal identification model, and determining a parameter optimization objective function; optimizing and solving the objective function by using a penalty function optimization algorithm to obtain optimized model parameters; according to the optimized model parameters and the electrocardiosignal recognition model, the electrocardiosignal characteristics extracted according to the preprocessing and the characteristics are used as model input, and then the processing and analysis results of the electrocardiosignals can be obtained. The invention realizes the intelligent processing of electrocardiosignals.

Description

Intelligent electrocardiosignal processing method
Technical Field
The invention relates to the technical field of signal processing, in particular to an intelligent electrocardiosignal processing method.
Background
The existing electrocardiosignal analysis processing generally analyzes the generated electrocardiosignal data, and the current heart rate condition of a patient is difficult to monitor in real time. The change condition of the electrocardiosignals of the patient is very complex, and particularly when the patient faces an emergency, the electrocardiosignals are collected for analysis, so that the precious rescue time of the patient is lost. To this end, the patent proposes a method of monitoring the heart rate of a patient in real time and performing a processing analysis.
The real-time electrocardiosignal processing scene has 3 main requirements, and firstly, the requirement on the running time is higher, so that the real-time electrocardiosignal processing scene needs a quick and low-delay service; secondly, the analysis usually needs to be operated for a long time without interruption, so that the analysis efficiency is required to be as fast as possible; thirdly, the electrocardiosignals need to be analyzed accurately in real time, so that the electrocardiosignals need to have the characteristics of high efficiency and intelligence. In order to meet the requirements, the patent provides an intelligent electrocardiosignal processing method for realizing rapid processing and analysis of electrocardiosignals.
Disclosure of Invention
The invention provides an intelligent electrocardiosignal processing method, aiming at (1) realizing rapid electrocardiosignal analysis and processing; (2) the electrocardiosignal processing efficiency is improved.
The invention provides an intelligent electrocardiosignal processing method, which comprises the following steps:
s1: acquiring electrocardiosignals and preprocessing the electrocardiosignals;
s2: extracting the characteristics of the preprocessed electrocardiosignals to obtain electrocardiosignal characteristics;
s3: taking the electrocardiosignal characteristics as input and the electrocardiosignal classification result as output, constructing a deep stack network electrocardiosignal identification model, and determining a parameter optimization objective function;
s4: optimizing and solving the objective function by using a penalty function optimization algorithm to obtain optimized model parameters;
s5: and (4) according to the optimized model parameters and the electrocardiosignal identification model obtained in the step (S4), the electrocardiosignal processed according to the step (S1-2) is used as model input, and then the processing and analysis result of the electrocardiosignal can be obtained.
As a further improvement of the method of the invention:
the step S1 is that electrocardiosignals are collected, a high-pass filter is constructed, and the electrocardiosignals are filtered by the high-pass filter, which comprises
Connecting the electrocardio electrode plate with the electrocardio sensor, fixing the electrocardio electrode plate on the skin of a human body, continuously acquiring electrocardiosignals of the human body by the electrocardio electrode plate, and transmitting the electrocardiosignals to the electrocardio sensor;
constructing a high-pass filter in a signal receiving portion of the electrocardiograph sensor, the high-pass filter including a capacitor and a resistor, the capacitor being connected in series with the electrocardiograph signal path and the resistor being connected in parallel with the signal path;
the high-pass filter allows the frequency of signals received by the electrocardio sensor to be higher than the cut-off frequency
Figure 100002_DEST_PATH_IMAGE002
The cut-off frequency of
Figure DEST_PATH_IMAGE002A
The calculation formula of (2) is as follows:
Figure 100002_DEST_PATH_IMAGE004
wherein:
r represents a resistance value of a resistor in the high-pass filter;
c denotes the capacitance of the capacitor in the high-pass filter.
In the step S1, performing noise reduction processing on the filtered electrocardiographic signal by using a noise reduction algorithm combined with IMF signal decomposition to obtain a preprocessed electrocardiographic signal, including:
performing EMD signal decomposition on the electrocardiosignals after filtering processing to obtain IMF components of the electrocardiosignals:
1) filtering the processed electrocardiosignal
Figure 100002_DEST_PATH_IMAGE006
Adding white noise of the same length
Figure 100002_DEST_PATH_IMAGE008
To obtain a noisy signal
Figure 100002_DEST_PATH_IMAGE010
：
Figure 100002_DEST_PATH_IMAGE012
2) Go through to obtain the signal
Figure DEST_PATH_IMAGE010A
Connecting all the maximum value points and all the minimum value points by cubic spline curves respectively to obtain signals
Figure DEST_PATH_IMAGE010AA
The upper envelope line and the lower envelope line are the mean value curves of the upper envelope line and the lower envelope line
Figure 100002_DEST_PATH_IMAGE014
Then, then
Figure 100002_DEST_PATH_IMAGE016
；
3) To pair
Figure 100002_DEST_PATH_IMAGE018
Performing the operation of step 2) to obtain
Figure 100002_DEST_PATH_IMAGE020
；
4) Repeating the step 3) 9 times to obtain
Figure 100002_DEST_PATH_IMAGE022
As a signal
Figure DEST_PATH_IMAGE010AAA
Of a first set of IMF components, wherein
Figure 100002_DEST_PATH_IMAGE024
The remaining components of the first set of IMF components
Figure 100002_DEST_PATH_IMAGE026
；
5) To pair
Figure 100002_DEST_PATH_IMAGE028
Carrying out steps 2) -4) to obtain k-1 group IMF components
Figure 100002_DEST_PATH_IMAGE030
And a residual component
Figure 100002_DEST_PATH_IMAGE032
(ii) a Will contain a noise signal
Figure DEST_PATH_IMAGE010AAAA
The decomposition is of the formula:
Figure 100002_DEST_PATH_IMAGE034
the IMF component obtained by decomposition
Figure 100002_DEST_PATH_IMAGE036
Performing fast Fourier transform to obtain frequency spectrum of IMF component:
Figure 100002_DEST_PATH_IMAGE038
wherein:
Figure 100002_DEST_PATH_IMAGE040
a frequency spectrum representing the i-th set of IMF components;
Figure 100002_DEST_PATH_IMAGE042
representing noisy electrocardiosignals
Figure DEST_PATH_IMAGE010_5A
The ith set of IMF components;
Figure 100002_DEST_PATH_IMAGE044
representing a fast fourier transform process;
if the frequency domain energy of 0-60Hz in the frequency spectrum of the IMF component reaches more than 90% of the energy of the whole frequency domain, judging the IMF component as the IMF component of the electrocardiosignal, otherwise, judging the IMF component as the IMF component of the noise signal, and deleting the IMF component of the noise signal;
performing signal reconstruction processing on all IMF components of the electrocardiosignals according to the following formula to obtain the electrocardiosignals after noise reduction:
Figure 100002_DEST_PATH_IMAGE046
wherein:
j represents the number of groups of components of the cardiac signal IMF.
The step S2 of extracting the electrocardiographic envelope characteristic of the preprocessed electrocardiographic signal includes:
for the preprocessed electrocardiosignals
Figure 100002_DEST_PATH_IMAGE048
And (3) carrying out normalization treatment:
Figure 100002_DEST_PATH_IMAGE050
to the electrocardiosignals after normalization processing
Figure 100002_DEST_PATH_IMAGE052
And (3) carrying out sectional processing, wherein each 30ms is a section of signal, a section of signal is taken every 20ms, the overlapping part between two adjacent sections of signals is a 10ms signal, and the entropy of each section of electrocardiosignal is as follows:
Figure 100002_DEST_PATH_IMAGE054
wherein:
n represents the number of sampled electrocardiosignal data of 30 ms;
taking the entropy of each section of electrocardiosignal as the electrocardio envelope characteristic of each section of electrocardiosignal, and determining the electrocardio envelope characteristic of the electrocardiosignal as
Figure 100002_DEST_PATH_IMAGE056
Wherein p represents the number of segments in the cardiac signal,
Figure 100002_DEST_PATH_IMAGE058
and representing the electrocardio envelope characteristics of the ith segment of electrocardiosignal.
The step of S2, extracting the electrocardiographic signal features from the electrocardiographic envelope features, includes:
characterizing the cardiac envelope
Figure DEST_PATH_IMAGE056A
As an input of the feature extraction network, a formula for extracting the characteristics of the electrical signal at the center of the electrocardio envelope characteristics by using the feature extraction network is as follows:
Figure 100002_DEST_PATH_IMAGE060
wherein:
Figure 100002_DEST_PATH_IMAGE062
the serial number of a hidden layer in the feature extraction network is shown, and a V-layer hidden layer is shared in the feature extraction network;
Figure DEST_PATH_IMAGE064
representing the features extracted at layer v in the feature extraction network,
Figure DEST_PATH_IMAGE066
representing the features extracted at level v-1 in the feature extraction network, when v equals 1,
Figure DEST_PATH_IMAGE068
representing the electrocardiogramEnvelope feature
Figure DEST_PATH_IMAGE070
；
Figure DEST_PATH_IMAGE072
Representing the weight of the v-th layer in the feature extraction network;
Figure DEST_PATH_IMAGE074
representing the bias of the v-th layer in the feature extraction network;
Figure DEST_PATH_IMAGE076
representing an activation function in the feature extraction network, wherein the selected activation function is a Sigmoid function;
and taking the features extracted by the V layer as the electrocardiosignal features L.
In the step S3, the electrocardiosignal features are used as input, the electrocardiosignal classification result is used as output, and a deep stack network electrocardiosignal identification model is constructed, including:
taking the electrocardiosignal characteristics as a training set of a deep stack network electrocardiosignal identification model, and labeling a signal classification label corresponding to the electrocardiosignal characteristics;
inputting the electrocardiosignal characteristics L into a deep stack network electrocardiosignal identification model, and outputting the prediction probability of the identification result of different electrocardiosignal characteristics by the deep stack network electrocardiosignal identification model:
Figure DEST_PATH_IMAGE078
Figure DEST_PATH_IMAGE080
wherein:
Figure DEST_PATH_IMAGE082
an electrocardiographic signal recognition result representing the prediction probability;
z represents the category number of the electrocardiosignal categories predicted by the deep stack network electrocardiosignal recognition model;
l represents the electrocardiosignal characteristics;
Figure DEST_PATH_IMAGE084
model parameters representing a deep stack network electrocardiosignal recognition model;
Figure DEST_PATH_IMAGE086
representing the predicted probability of the s-th electrocardiosignal;
Figure DEST_PATH_IMAGE088
and the weight vector represents the output matrix of the s category in the softmax classifier.
The parameter optimization objective function of the deep stack network electrocardiosignal identification model constructed in the step S3 comprises the following steps:
the parameter optimization objective function of the built deep stack network electrocardiosignal identification model is as follows:
Figure DEST_PATH_IMAGE090
wherein:
y represents the number of training samples,
Figure DEST_PATH_IMAGE092
representing the ith sample in the training sample set;
z represents the number of categories identifying the category of the cardiac signal;
Figure DEST_PATH_IMAGE094
representing the attenuation coefficient of the weight vector,
Figure DEST_PATH_IMAGE096
it is set to 0.8;
Figure DEST_PATH_IMAGE098
representing samples of an electrocardiosignal
Figure DEST_PATH_IMAGE092A
Identifying results in the deep stack network electrocardiosignal identification model;
Figure DEST_PATH_IMAGE100
representing samples of an electrocardiographic signal
Figure DEST_PATH_IMAGE092AA
True signal classification tags of (1);
Figure DEST_PATH_IMAGE102
representing samples of an electrocardiosignal
Figure DEST_PATH_IMAGE092AAA
A weight vector of a jth category output matrix;
the constraint conditions of the constructed parameter optimization objective function are as follows:
Figure DEST_PATH_IMAGE104
Figure DEST_PATH_IMAGE106
wherein:
Figure DEST_PATH_IMAGE108
and the weight vector represents the output matrix of the jth category in the softmax classifier.
In the step S4, performing optimization solution on the parameter optimization objective function by using a penalty function optimization algorithm, including:
converting the parameter optimization objective function as follows:
Figure DEST_PATH_IMAGE110
wherein:
Figure DEST_PATH_IMAGE112
，
Figure DEST_PATH_IMAGE114
representing samples of an electrocardiographic signal
Figure DEST_PATH_IMAGE116
The probability parameter of (a) is determined,
Figure DEST_PATH_IMAGE102A
representing samples of an electrocardiographic signal
Figure DEST_PATH_IMAGE092AAAA
A weight vector of a jth category output matrix;
calculating a penalty term for the objective function:
Figure DEST_PATH_IMAGE118
Figure DEST_PATH_IMAGE120
constructing a penalty function of the parameter optimization objective function:
Figure DEST_PATH_IMAGE122
wherein:
Figure DEST_PATH_IMAGE124
a penalty factor representing a penalty function, set to 20;
solving the penalty function when
Figure DEST_PATH_IMAGE126
And the value of H is the optimized model parameter value of the deep stack network electrocardiosignal identification model.
In the step S5, according to the optimized model parameters and the electrocardiosignal recognition model obtained in the step S4, the electrocardiosignal processed in the step S1-2 is input as a model to obtain an electrocardiosignal recognition result, which includes:
taking the optimized model parameters as model parameters of the deep stack network electrocardiosignal recognition model to obtain the optimized deep stack network electrocardiosignal recognition model;
and (4) processing the electrocardiosignals according to the electrocardiosignal processing method of the steps S1-S2 to obtain electrocardiosignal characteristics, inputting the electrocardiosignal characteristics as a model of a deep stack network electrocardiosignal identification model, and outputting an electrocardiosignal identification result with the maximum prediction probability by the deep stack network electrocardiosignal identification model.
Compared with the prior art, the invention provides an intelligent electrocardiosignal processing method, which has the following advantages:
firstly, the scheme provides an electrocardiosignal preprocessing flow, a high-pass filter is constructed in a signal receiving part in an electrocardio sensor, the high-pass filter comprises a capacitor and a resistor, the capacitor is connected with an electrocardiosignal path in series, and the resistor is connected with the signal path in parallel, so that the high-pass filter allows the electrocardiosignal receiving frequency of the electrocardio sensor to be higher than a cut-off frequency
Figure DEST_PATH_IMAGE002AA
The received signal frequencies are all higher than the cut-off frequency, so that part of low-frequency noise signals are filtered; meanwhile, the electrocardiosignal is subjected to noise reduction by utilizing a noise reduction algorithm combined with IMF signal decomposition, and the center frequency of an IMF component generated by decomposition is divided into two when an EMD method is used for processing Gaussian white noiseThe multiplied speed is reduced, the frequency range is increased at double speed, Gaussian white noise is added into the original signal, the original signal can be decomposed into a series of signals with different scales according to binary characteristics, elimination of noise signals with various scales can be realized, most of energy of the electrocardiosignal is in the frequency domain range of 0-50Hz, if the frequency domain energy of 0-60Hz in the IMF frequency spectrum after FFT processing does not reach more than 90% of the energy of the whole frequency domain, the IMF component can be regarded as the IMF component of the noise signal, and the noise reduction processing of the electrocardiosignal is realized by deleting the IMF component.
Meanwhile, according to the scheme, the electrocardiosignal features are extracted by using the feature extraction network, the electrocardiosignal features are used as input, the electrocardiosignal recognition result is used as output, a deep stack network electrocardiosignal recognition model is built, a deep stack network electrocardiosignal recognition model optimization strategy is provided, and a target function is optimized by building parameters of the deep stack network electrocardiosignal recognition model:
Figure DEST_PATH_IMAGE090A
wherein: y represents the number of training samples and,
Figure DEST_PATH_IMAGE128
representing the ith sample in the training sample set; z represents the number of categories identifying the category of the cardiac signal;
Figure DEST_PATH_IMAGE130
representing the attenuation coefficient of the weight vector,
Figure DEST_PATH_IMAGE132
it is set to 0.8;
Figure DEST_PATH_IMAGE134
representing samples of an electrocardiographic signal
Figure DEST_PATH_IMAGE128A
Identifying results in the deep stack network electrocardiosignal identification model;
Figure DEST_PATH_IMAGE136
representing samples of an electrocardiographic signal
Figure DEST_PATH_IMAGE128AA
True signal classification tags of (1);
Figure DEST_PATH_IMAGE138
representing samples of an electrocardiosignal
Figure DEST_PATH_IMAGE128AAA
A weight vector of a jth category output matrix; the constraint conditions of the constructed parameter optimization objective function are as follows:
Figure DEST_PATH_IMAGE104A
Figure DEST_PATH_IMAGE106A
wherein:
Figure DEST_PATH_IMAGE140
a weight vector representing the output matrix of the jth category in the softmax classifier; and converting the parameter optimization objective function as follows:
Figure DEST_PATH_IMAGE110A
wherein:
Figure DEST_PATH_IMAGE142
，
Figure DEST_PATH_IMAGE144
representing samples of an electrocardiographic signal
Figure DEST_PATH_IMAGE146
The probability parameter of (a) is,
Figure DEST_PATH_IMAGE138A
representing samples of an electrocardiographic signal
Figure DEST_PATH_IMAGE128AAAA
A weight vector of a jth category output matrix; calculating a penalty term for the objective function:
Figure DEST_PATH_IMAGE118A
Figure DEST_PATH_IMAGE120A
constructing a penalty function of the parameter optimization objective function:
Figure DEST_PATH_IMAGE122A
wherein:
Figure DEST_PATH_IMAGE148
a penalty factor representing a penalty function, set to 20; solving the penalty function when
Figure DEST_PATH_IMAGE150
And the value of H is the optimized model parameter value of the deep stack network electrocardiosignal identification model. According to the scheme, the parameter optimization objective function of the deep stack network electrocardiosignal recognition model with the constraint condition is constructed, and the parameter optimization objective function with the constraint condition is converted into the unconstrained objective function by using a penalty function method, so that the parameter optimization value in the objective function is calculated by using a function solving method, the parameter optimization process is simplified, and the training efficiency of the deep stack network electrocardiosignal recognition model is improved.
Drawings
Fig. 1 is a schematic flow chart of an intelligent electrocardiographic signal processing method according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1:
s1: collecting electrocardiosignals and preprocessing the electrocardiosignals.
The step S1 is that electrocardiosignals are collected, a high-pass filter is constructed, and the electrocardiosignals are filtered by the high-pass filter, which comprises
Connecting the electrocardio electrode plate with the electrocardio sensor, fixing the electrocardio electrode plate on the skin of a human body, continuously acquiring electrocardiosignals of the human body by the electrocardio electrode plate, and transmitting the electrocardiosignals to the electrocardio sensor;
constructing a high-pass filter in a signal receiving part in the electrocardio sensor, wherein the high-pass filter comprises a capacitor and a resistor, the capacitor is connected with an electrocardio signal path in series, and the resistor is connected with the signal path in parallel;
the high-pass filter allows the frequency of signals received by the electrocardio sensor to be higher than the cut-off frequency
Figure DEST_PATH_IMAGE002AAA
Said cut-off frequency of the cardiac signal
Figure DEST_PATH_IMAGE002AAAA
The calculation formula of (2) is as follows:
Figure DEST_PATH_IMAGE004A
wherein:
r represents a resistance value of a resistor in the high-pass filter;
c denotes the capacitance of the capacitor in the high-pass filter.
In the step S1, performing noise reduction processing on the filtered electrocardiographic signal by using a noise reduction algorithm combined with IMF signal decomposition to obtain a preprocessed electrocardiographic signal, including:
performing EMD signal decomposition on the electrocardiosignals after filtering processing to obtain IMF components of the electrocardiosignals:
1) filtering the processed electrocardiosignal
Figure DEST_PATH_IMAGE006A
Adding white noise of the same length
Figure DEST_PATH_IMAGE008A
Obtaining a noisy signal
Figure DEST_PATH_IMAGE010_6A
：
Figure DEST_PATH_IMAGE012A
2) Go through to obtain the signal
Figure DEST_PATH_IMAGE010_7A
Connecting all the maximum value points and all the minimum value points by cubic spline curves respectively to obtain signals
Figure DEST_PATH_IMAGE010_8A
The upper envelope line and the lower envelope line are the mean value curves of the upper envelope line and the lower envelope line
Figure DEST_PATH_IMAGE014A
Then, then
Figure DEST_PATH_IMAGE016A
；
3) To pair
Figure DEST_PATH_IMAGE018A
Performing the operation of step 2) to obtain
Figure DEST_PATH_IMAGE020A
；
4) Repeating the step 3) 9 times to obtain
Figure DEST_PATH_IMAGE022A
As a signal
Figure DEST_PATH_IMAGE010_9A
Of a first set of IMF components, wherein
Figure DEST_PATH_IMAGE024A
The remaining components of the first set of IMF components
Figure DEST_PATH_IMAGE026A
；
5) To pair
Figure DEST_PATH_IMAGE028A
Carrying out steps 2) -4) to obtain k-1 group IMF components
Figure DEST_PATH_IMAGE030A
And a residual component
Figure DEST_PATH_IMAGE032A
(ii) a Will contain a noisy signal
Figure DEST_PATH_IMAGE010_10A
The decomposition is of the formula:
Figure DEST_PATH_IMAGE034A
the IMF component obtained by decomposition
Figure DEST_PATH_IMAGE036A
Performing fast Fourier transform to obtain frequency spectrum of IMF component:
Figure DEST_PATH_IMAGE038A
wherein:
Figure DEST_PATH_IMAGE040A
a frequency spectrum representing the i-th set of IMF components;
Figure DEST_PATH_IMAGE042A
representing noisy electrocardiosignals
Figure DEST_PATH_IMAGE010_11A
The ith set of IMF components;
Figure DEST_PATH_IMAGE044A
representing a fast fourier transform process;
if the frequency domain energy of 0-60Hz in the frequency spectrum of the IMF component reaches more than 90% of the energy of the whole frequency domain, judging the IMF component as the IMF component of the electrocardiosignal, otherwise, judging the IMF component as the IMF component of the noise signal, and deleting the IMF component of the noise signal;
performing signal reconstruction processing on all IMF components of the electrocardiosignals according to the following formula to obtain the electrocardiosignals after noise reduction:
Figure DEST_PATH_IMAGE046A
wherein:
j represents the number of groups of components of the cardiac signal IMF.
S2: and extracting the characteristics of the preprocessed electrocardiosignals to obtain the electrocardiosignal characteristics.
The step of S2, extracting the ecg envelope characteristics of the preprocessed ecg signal, includes:
for the preprocessed electrocardiosignals
Figure DEST_PATH_IMAGE048A
And (3) carrying out normalization treatment:
Figure DEST_PATH_IMAGE050A
to the electrocardio after normalization processingSignal
Figure DEST_PATH_IMAGE052A
And (3) carrying out sectional processing, wherein each 30ms is a section of signal, a section of signal is taken every 20ms, the overlapping part between two adjacent sections of signals is a 10ms signal, and the entropy of each section of electrocardiosignal is as follows:
Figure DEST_PATH_IMAGE054A
wherein:
n represents the number of sampled electrocardiosignal data of 30 ms;
taking the entropy of each section of electrocardiosignal as the electrocardio envelope characteristic of each section of electrocardiosignal, and determining the electrocardio envelope characteristic of the electrocardiosignal as
Figure DEST_PATH_IMAGE056AA
Wherein p represents the number of segments in the cardiac signal,
Figure DEST_PATH_IMAGE058A
and representing the electrocardio envelope characteristics of the ith segment of electrocardiosignal.
S3: and (3) taking the electrocardiosignal characteristics as input and the electrocardiosignal classification result as output, constructing a deep stack network electrocardiosignal identification model, and determining a parameter optimization objective function.
In the step S3, the electrocardiosignal features are used as input, the electrocardiosignal classification result is used as output, and a deep stack network electrocardiosignal identification model is constructed, including:
taking the electrocardiosignal characteristics as a training set of a deep stack network electrocardiosignal identification model, and labeling a signal classification label corresponding to the electrocardiosignal characteristics;
inputting the electrocardiosignal characteristics L into a deep stack network electrocardiosignal identification model, and outputting the prediction probability of the identification result of different electrocardiosignal characteristics by the deep stack network electrocardiosignal identification model:
Figure DEST_PATH_IMAGE078A
Figure DEST_PATH_IMAGE080A
wherein:
Figure DEST_PATH_IMAGE082A
an electrocardiographic signal recognition result representing the prediction probability;
z represents the category number of the electrocardiosignal categories predicted by the deep stack network electrocardiosignal recognition model;
l represents the electrocardiosignal characteristics;
Figure DEST_PATH_IMAGE084A
model parameters representing a deep stack network electrocardiosignal identification model;
Figure DEST_PATH_IMAGE086A
representing the predicted probability of the s-th electrocardiosignal;
Figure DEST_PATH_IMAGE088A
and the weight vector represents the output matrix of the s category in the softmax classifier.
The parameter optimization objective function of the deep stack network electrocardiosignal identification model constructed in the step S3 comprises the following steps:
the parameter optimization objective function of the built deep stack network electrocardiosignal identification model is as follows:
Figure DEST_PATH_IMAGE090AA
wherein:
y represents the number of training samples,
Figure DEST_PATH_IMAGE092_5A
representing the ith sample in the training sample set;
z represents the number of categories identifying the category of the cardiac signal;
Figure DEST_PATH_IMAGE094A
representing the attenuation coefficient of the weight vector,
Figure DEST_PATH_IMAGE096A
it is set to 0.8;
Figure DEST_PATH_IMAGE098A
representing samples of an electrocardiographic signal
Figure DEST_PATH_IMAGE092_6A
Identifying results in the deep stack network electrocardiosignal identification model;
Figure DEST_PATH_IMAGE100A
representing samples of an electrocardiographic signal
Figure DEST_PATH_IMAGE092_7A
True signal classification tags of (1);
Figure DEST_PATH_IMAGE102AA
representing samples of an electrocardiographic signal
Figure DEST_PATH_IMAGE092_8A
A weight vector of a jth category output matrix;
the constraint conditions of the constructed parameter optimization objective function are as follows:
Figure DEST_PATH_IMAGE104AA
Figure DEST_PATH_IMAGE106AA
wherein:
Figure DEST_PATH_IMAGE108A
a weight vector representing the output matrix of the jth class in the softmax classifier.
S4: and (5) optimizing and solving the objective function by using a penalty function optimization algorithm to obtain optimized model parameters.
Converting the parameter optimization objective function as follows:
Figure DEST_PATH_IMAGE110AA
wherein:
Figure DEST_PATH_IMAGE112A
，
Figure DEST_PATH_IMAGE114A
representing samples of an electrocardiographic signal
Figure DEST_PATH_IMAGE116A
The probability parameter of (a) is,
Figure DEST_PATH_IMAGE102AAA
representing samples of an electrocardiosignal
Figure DEST_PATH_IMAGE092_9A
A weight vector of a jth category output matrix;
calculating a penalty term for the objective function:
Figure DEST_PATH_IMAGE118AA
Figure DEST_PATH_IMAGE120AA
constructing a penalty function of the parameter optimization objective function:
Figure DEST_PATH_IMAGE122AA
wherein:
Figure DEST_PATH_IMAGE124A
a penalty factor representing a penalty function, set to 20;
solving the penalty function when
Figure DEST_PATH_IMAGE126A
And the value of H is the optimized model parameter value of the deep stack network electrocardiosignal identification model.
In the step S5, according to the optimized model parameters and the electrocardiosignal recognition model obtained in the step S4, the electrocardiosignal processed in the step S1-2 is input as a model to obtain an electrocardiosignal recognition result, which includes:
taking the optimized model parameters as model parameters of the deep stack network electrocardiosignal recognition model to obtain the optimized deep stack network electrocardiosignal recognition model;
and (4) processing the electrocardiosignals according to the electrocardiosignal processing method of the steps S1-S2 to obtain electrocardiosignal characteristics, inputting the electrocardiosignal characteristics as a model of a deep stack network electrocardiosignal identification model, and outputting an electrocardiosignal identification result with the maximum prediction probability by the deep stack network electrocardiosignal identification model.
Example 2:
this example is substantially the same as example 1, except that:
s2: and extracting the characteristics of the preprocessed electrocardiosignals to obtain the electrocardiosignal characteristics.
The step of S2, extracting the electrocardiographic signal feature in the electrocardiographic envelope feature, includes:
characterizing the cardiac envelope
Figure DEST_PATH_IMAGE056AAA
As an input of the feature extraction network, a formula for extracting the characteristics of the electrical signal at the center of the electrocardio envelope characteristics by using the feature extraction network is as follows:
Figure DEST_PATH_IMAGE060A
wherein:
Figure DEST_PATH_IMAGE062A
the serial number of a hidden layer in the feature extraction network is shown, and a V-layer hidden layer is shared in the feature extraction network;
Figure DEST_PATH_IMAGE064A
representing the features extracted at layer v in the feature extraction network,
Figure DEST_PATH_IMAGE066A
representing the features extracted at level v-1 in the feature extraction network, when v equals 1,
Figure DEST_PATH_IMAGE068A
representing features of an envelope of the electrocardiogram
Figure DEST_PATH_IMAGE070A
；
Figure DEST_PATH_IMAGE072A
Representing the weight of the v-th layer in the feature extraction network;
Figure DEST_PATH_IMAGE074A
representing the bias of the v-th layer in the feature extraction network;
Figure DEST_PATH_IMAGE076A
representing activation functions in a feature extraction network, selected activation functionsIs a Sigmoid function;
and taking the features extracted by the V layer as the electrocardiosignal features L.
It should be noted that the above-mentioned numbers of the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (7)

1. An intelligent electrocardiosignal processing method is characterized by comprising the following steps:
s1: acquiring electrocardiosignals and preprocessing the electrocardiosignals;
s2: extracting the features of the preprocessed electrocardiosignals to obtain the electrocardiosignal features, and extracting the electrocardio envelope features of the preprocessed electrocardiosignals, wherein the extracting comprises the following steps:
for the preprocessed electrocardiosignals
Figure DEST_PATH_IMAGE001
And (3) carrying out normalization treatment:
Figure DEST_PATH_IMAGE002
to the electrocardiosignals after normalization processing
Figure DEST_PATH_IMAGE003
And (3) carrying out sectional processing, wherein each 30ms is a section of signal, a section of signal is taken every 20ms, the overlapping part between two adjacent sections of signals is a 10ms signal, and the entropy of each section of electrocardiosignal is as follows:
Figure DEST_PATH_IMAGE004
wherein:
n represents the number of sampled electrocardiosignal data of 30 ms;
taking the entropy of each section of electrocardiosignal as the electrocardio envelope characteristic of each section of electrocardiosignal, and determining the electrocardio envelope characteristic of the electrocardiosignal as
Figure DEST_PATH_IMAGE005
Wherein p represents the number of segments in the cardiac signal,
Figure DEST_PATH_IMAGE006
representing the electrocardio envelope characteristics of the ith segment of electrocardiosignal;
extracting electrocardiosignal characteristics in the electrocardio envelope characteristics, comprising:
characterizing the cardiac envelope
Figure 258794DEST_PATH_IMAGE005
As an input of the feature extraction network, a formula for extracting the characteristics of the electrical signal at the center of the electrocardio envelope characteristics by using the feature extraction network is as follows:
Figure DEST_PATH_IMAGE007
wherein:
Figure DEST_PATH_IMAGE008
indicating the sequence number of a hidden layer in the feature extraction network;
Figure DEST_PATH_IMAGE009
representing the features extracted at layer v in the feature extraction network,
Figure DEST_PATH_IMAGE010
representing the features extracted at level v-1 in the feature extraction network, when v equals 1,
Figure DEST_PATH_IMAGE011
representing features of an envelope of the electrocardiogram
Figure DEST_PATH_IMAGE012
；
Figure DEST_PATH_IMAGE013
Representing weights at layer v in the feature extraction network;
Figure DEST_PATH_IMAGE014
representing the bias of the v-th layer in the feature extraction network;
Figure DEST_PATH_IMAGE015
representing an activation function in a feature extraction network;
taking the features extracted from the V layer as electrocardiosignal features L;
s3: taking the electrocardiosignal characteristics as input and the electrocardiosignal classification result as output, constructing a deep stack network electrocardiosignal identification model, and determining a parameter optimization objective function;
s4: optimizing and solving the objective function by using a penalty function optimization algorithm to obtain optimized model parameters;
s5: and (4) according to the optimized model parameters and the electrocardiosignal identification model obtained in the step (S4), the electrocardiosignal processed according to the step (S1-2) is used as model input, and then the processing and analysis result of the electrocardiosignal can be obtained.
2. The method as claimed in claim 1, wherein said step S1 is a step of collecting electrocardiosignals, constructing a high-pass filter, and performing filtering process on the electrocardiosignals by using the high-pass filter, comprising
Connecting the electrocardio electrode plate with the electrocardio sensor, fixing the electrocardio electrode plate on the skin of a human body, continuously acquiring electrocardiosignals of the human body by the electrocardio electrode plate, and transmitting the electrocardiosignals to the electrocardio sensor;
a signal receiving part in the electrocardio sensor is provided with a high-pass filter, the high-pass filter comprises a capacitor and a resistor, the capacitor is connected with an electrocardio signal path in series, and the resistor is connected with the signal path in parallel;
the high-pass filter allows the frequency of signals received by the electrocardio sensor to be higher than the cut-off frequency
Figure DEST_PATH_IMAGE016
The cut-off frequency of
Figure 545681DEST_PATH_IMAGE016
The calculation formula of (2) is as follows:
Figure DEST_PATH_IMAGE017
wherein:
r represents a resistance value of a resistor in the high-pass filter;
c denotes the capacitance of the capacitor in the high-pass filter.
3. The intelligent electrocardiosignal processing method according to claim 2, wherein in the step S1, the denoising algorithm combined with IMF signal decomposition is used for denoising the filtered electrocardiosignal to obtain the preprocessed electrocardiosignal, and the method comprises:
performing EMD signal decomposition on the electrocardiosignals after filtering processing to obtain IMF components of the electrocardiosignals:
1) filtering the processed electrocardiosignal
Figure DEST_PATH_IMAGE018
Adding white noise of the same length
Figure DEST_PATH_IMAGE019
To obtain a noisy signal
Figure DEST_PATH_IMAGE020
：
Figure DEST_PATH_IMAGE021
2) Go through to obtain the signal
Figure 91545DEST_PATH_IMAGE020
Connecting all the maximum value points and all the minimum value points by cubic spline curves respectively to obtain signals
Figure 382849DEST_PATH_IMAGE020
Upper envelope and lower envelope ofThe mean curve of the upper and lower envelope curves is
Figure DEST_PATH_IMAGE022
Then, then
Figure DEST_PATH_IMAGE023
；
3) For is to
Figure DEST_PATH_IMAGE024
Performing the operation of step 2) to obtain
Figure DEST_PATH_IMAGE025
；
4) Repeating the step 3) 9 times to obtain
Figure DEST_PATH_IMAGE026
As a signal
Figure 259669DEST_PATH_IMAGE020
Of a first set of IMF components, wherein
Figure DEST_PATH_IMAGE027
The remaining components of the first set of IMF components
Figure DEST_PATH_IMAGE028
；
5) To pair
Figure DEST_PATH_IMAGE029
Carrying out steps 2) -4) to obtain k-1 group IMF components
Figure DEST_PATH_IMAGE030
And a residual component
Figure DEST_PATH_IMAGE031
(ii) a Will contain a noise signal
Figure 943722DEST_PATH_IMAGE020
The decomposition is of the formula:
Figure DEST_PATH_IMAGE032
the IMF component obtained by decomposition
Figure DEST_PATH_IMAGE033
Performing fast Fourier transform to obtain frequency spectrum of IMF component:
Figure DEST_PATH_IMAGE034
wherein:
Figure DEST_PATH_IMAGE035
a frequency spectrum representing the i-th set of IMF components;
Figure DEST_PATH_IMAGE036
representing noisy electrocardiosignals
Figure 219458DEST_PATH_IMAGE020
The ith set of IMF components;
Figure DEST_PATH_IMAGE037
representing a fast fourier transform process;
if the frequency domain energy of 0-60Hz in the frequency spectrum of the IMF component reaches more than 90% of the energy of the whole frequency domain, judging the IMF component as the IMF component of the electrocardiosignal, otherwise, judging the IMF component as the IMF component of the noise signal, and deleting the IMF component of the noise signal;
performing signal reconstruction processing on all IMF components of the electrocardiosignals according to the following formula to obtain the electrocardiosignals after noise reduction:
Figure DEST_PATH_IMAGE038
wherein:
j represents the number of groups of components of the cardiac signal IMF.
4. The intelligent electrocardiosignal processing method according to claim 1, wherein the step S3 is implemented by constructing a deep stack network electrocardiosignal recognition model by taking electrocardiosignal characteristics as input and electrocardiosignal classification results as output, and comprises the steps of:
taking the electrocardiosignal characteristics as a training set of a deep stack network electrocardiosignal identification model, and labeling a signal classification label corresponding to the electrocardiosignal characteristics;
inputting the electrocardiosignal characteristics L into a deep stack network electrocardiosignal identification model, and outputting the prediction probability of the identification result of different electrocardiosignal characteristics by the deep stack network electrocardiosignal identification model:
Figure DEST_PATH_IMAGE039
Figure DEST_PATH_IMAGE040
wherein:
Figure DEST_PATH_IMAGE041
an electrocardiographic signal recognition result representing the prediction probability;
z represents the category number of the electrocardiosignal categories predicted by the deep stack network electrocardiosignal recognition model;
l represents the electrocardiosignal characteristics;
Figure DEST_PATH_IMAGE042
representing deep stacked network coresThe electric signal identifies model parameters of the model;
Figure DEST_PATH_IMAGE043
representing the predicted probability of the s-th electrocardiosignal;
Figure DEST_PATH_IMAGE044
and the weight vector represents the output matrix of the s category in the softmax classifier.
5. The intelligent cardiac signal processing method according to claim 1, wherein the step S3 of constructing a parameter optimization objective function of the deep stack network cardiac signal identification model includes:
the parameter optimization objective function of the built deep stack network electrocardiosignal identification model is as follows:
Figure DEST_PATH_IMAGE045
wherein:
y represents the number of training samples,
Figure DEST_PATH_IMAGE046
representing the electrocardiosignal characteristics of the ith sample in the training sample set;
z represents the number of categories identifying the category of the cardiac signal;
Figure DEST_PATH_IMAGE047
representing the attenuation coefficient of the weight vector,
Figure DEST_PATH_IMAGE048
it is set to 0.8;
Figure DEST_PATH_IMAGE049
representing samples of an electrocardiographic signal
Figure 760292DEST_PATH_IMAGE046
Identifying results in the deep stack network electrocardiosignal identification model;
Figure DEST_PATH_IMAGE050
representing samples of an electrocardiographic signal
Figure 858829DEST_PATH_IMAGE046
True signal classification tags of (1);
Figure DEST_PATH_IMAGE051
representing samples of an electrocardiographic signal
Figure 799103DEST_PATH_IMAGE046
A weight vector of a jth category output matrix;
the constraint conditions of the constructed parameter optimization objective function are as follows:
Figure DEST_PATH_IMAGE052
Figure DEST_PATH_IMAGE053
wherein:
Figure DEST_PATH_IMAGE054
and the weight vector represents the output matrix of the jth category in the softmax classifier.
6. The intelligent cardiac signal processing method as set forth in claim 5, wherein the step S4 of performing optimization solution on the parameter optimization objective function by using a penalty function optimization algorithm includes:
and converting the parameter optimization objective function as follows:
Figure DEST_PATH_IMAGE055
wherein:
Figure DEST_PATH_IMAGE056
，
Figure DEST_PATH_IMAGE057
representing samples of an electrocardiographic signal
Figure DEST_PATH_IMAGE058
The probability parameter of (a) is,
Figure 522821DEST_PATH_IMAGE051
representing samples of an electrocardiographic signal
Figure 296873DEST_PATH_IMAGE046
A weight vector of a jth category output matrix;
calculating a penalty term for the objective function:
Figure DEST_PATH_IMAGE059
Figure DEST_PATH_IMAGE060
constructing a penalty function of the parameter optimization objective function:
Figure DEST_PATH_IMAGE061
wherein:
Figure DEST_PATH_IMAGE062
a penalty factor representing a penalty function, set to 20;
solving the penalty function when
Figure DEST_PATH_IMAGE063
And the value of H is the optimized model parameter value of the deep stack network electrocardiosignal identification model.
7. The intelligent method as claimed in claim 6, wherein the step S5, based on the optimized model parameters and the ecg signal recognition model obtained in step S4, obtains the recognition result of the ecg signal by inputting the ecg signal processed in step S1-2 as a model, and comprises:
taking the optimized model parameters as model parameters of the deep stack network electrocardiosignal recognition model to obtain the optimized deep stack network electrocardiosignal recognition model;
and (4) processing the electrocardiosignals according to the electrocardiosignal processing method of the steps S1-S2 to obtain electrocardiosignal characteristics, inputting the electrocardiosignal characteristics as a model of a deep stack network electrocardiosignal identification model, and outputting an electrocardiosignal identification result with the maximum prediction probability by the deep stack network electrocardiosignal identification model.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007053975A1 (en) * 2005-11-09 2007-05-18 Iris Zhao A mobile terminal, a medical sevice system and an ecg monitoring method for monitoring
CN107007279A (en) * 2017-03-17 2017-08-04 浙江大学 A kind of noninvasive intracardiac exciting independent positioning method of exception based on stacking-type self-encoding encoder
CN110801221A (en) * 2019-12-09 2020-02-18 中山大学 Sleep apnea fragment detection method and device based on unsupervised feature learning
CN110974213A (en) * 2019-12-20 2020-04-10 哈尔滨理工大学 Electrocardiosignal identification method based on deep stack network

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104523266B (en) * 2015-01-07 2017-04-05 河北大学 A kind of electrocardiosignal automatic classification method
CN105117736B (en) * 2015-08-18 2018-04-17 西安电子科技大学 Classification of Polarimetric SAR Image method based on sparse depth heap stack network
US10685429B2 (en) * 2017-02-22 2020-06-16 Siemens Healthcare Gmbh Denoising medical images by learning sparse image representations with a deep unfolding approach
CN108470156B (en) * 2018-03-06 2021-09-28 南京邮电大学 Heart sound signal classification and identification method
CN108670291A (en) * 2018-06-01 2018-10-19 四川长虹电器股份有限公司 The heart sound kind identification method of improved MFCC is combined based on EMD
CN109602416A (en) * 2019-01-09 2019-04-12 山东省计算中心（国家超级计算济南中心） A kind of method of ECG signal joint baseline correction and noise reduction
CN111832537B (en) * 2020-07-27 2023-04-25 深圳竹信科技有限公司 Abnormal electrocardiosignal identification method and abnormal electrocardiosignal identification device

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007053975A1 (en) * 2005-11-09 2007-05-18 Iris Zhao A mobile terminal, a medical sevice system and an ecg monitoring method for monitoring
CN107007279A (en) * 2017-03-17 2017-08-04 浙江大学 A kind of noninvasive intracardiac exciting independent positioning method of exception based on stacking-type self-encoding encoder
CN110801221A (en) * 2019-12-09 2020-02-18 中山大学 Sleep apnea fragment detection method and device based on unsupervised feature learning
CN110974213A (en) * 2019-12-20 2020-04-10 哈尔滨理工大学 Electrocardiosignal identification method based on deep stack network

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于香农信息熵的心电R波检测算法;苏嘉豪等;《电子世界》;20170423(第08期);第59-64页 *

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