CN106214145A - A kind of electrocardiogram classification method based on degree of depth learning algorithm - Google Patents
A kind of electrocardiogram classification method based on degree of depth learning algorithm Download PDFInfo
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Abstract
The invention discloses a kind of electrocardiogram classification method based on degree of depth learning algorithm, comprise the following steps: a. obtains measurement time original electrocardiographicdigital figure Wave data more than 8 seconds, electrocardiogram additional information and obtains electrocardiogram rhythm and pace of moving things information according to original electrocardiographicdigital figure Wave data, represent PQRST Wave data；B. the neutral net of degree of depth learning algorithm is trained and by electrocardiogram rhythm and pace of moving things information, represent PQRST Wave data, electrocardiogram additional information be arranged in one-dimensional data after carry out waveform separation by the degree of depth learning algorithm trained, obtain electrocardiogram classification result.The present invention imports electrocardiogram classification field degree of depth learning method, reasonably combine the feature of electrocardiogram classification, and by above step degree of depth learning method be trained and carry out waveform separation with degree of depth learning method, it is possible to increase substantially the information quality that electrocardiogram classification auxiliary information is provided to doctor.
Description
Technical field
The present invention relates to electrocardiogram classification method, particularly relate to a kind of electrocardiogram classification side based on degree of deep learning algorithm
Method.
Background technology
Electrocardiogram waveform data collection and electrocardiogram classification result are the important supplementary meanss of diagnosis heart disease illness
And reference information, usual electrocardiogram waveform data collection and classification are to carry out in hospital or MEC, exist detection inconvenient,
Detection shortcomings such as frequency is low, and in time electrocardiogram classification information can not be supplied to doctor to do real-time diagnosis, are difficult to have
The prevention of effect ground and in time treatment heart disease pathological changes.In recent years, along with network, intelligent movable mobile phone universal so that the portable type heart
The release of pyroelectric monitor instrument, household person electric wave monitor diligently is possibly realized.This kind of monitor released in the market, due to
Its sorting algorithm is based on traditional ecg measurement classification method, easily occurs by mistake when single waveform measurement feature is less obvious
Classification, its clinical reliability and accuracy are relatively low, it is impossible to meet the needs of the actual information that provides assistance in diagnosis to doctor.
Summary of the invention
The present invention is directed to ecg measurement classification method traditional present in prior art in single waveform measurement feature not
Misclassification easily occur time too substantially, its clinical reliability and accuracy are relatively low, it is impossible to meet reality provides auxiliary to doctor
Diagnostic message the defect such as need, it is provided that a kind of new electrocardiogram classification method based on degree of deep learning algorithm.
In order to solve above-mentioned technical problem, the present invention is achieved through the following technical solutions:
A kind of electrocardiogram classification method based on degree of deep learning algorithm, comprises the following steps:
A. measurement time original electrocardiographicdigital figure Wave data more than 8 seconds, electrocardiogram additional information are obtained, and according to original electrocardiographicdigital
Figure Wave data carries out the extraction of electrocardiogram rhythm and pace of moving things information, represents the extraction of PQRST waveform, obtains electrocardiogram rhythm and pace of moving things information, generation
Table PQRST Wave data；
B. the neutral net of degree of deep learning algorithm is trained, the electrocardiogram rhythm and pace of moving things information that step a obtained, represents PQRST
Wave data, electrocardiogram additional information carry out waveform separation by the degree of deep learning algorithm trained after being arranged in one-dimensional data,
Obtain electrocardiogram classification result.
Degree of deep learning algorithm is the machine learning method of a kind of artificial intelligence field, and it contains the multilayer perception of many hidden layers
Device, is to form more abstract high-rise expression attribute classification or feature, to find the distributed of data by combination low-level feature
Character representation, degree of deep learning method, at present the most in image recognition, has been proved to the effective of it in the application such as voice recognition
Property, it is possible to the accuracy of identification of traditional method is greatly improved.The present invention imports electrocardiogram classification neck degree of deep learning method
Territory, reasonably combines the feature of electrocardiogram classification, and is trained degree of deep learning method by above step and by the degree of depth
Learning method carries out waveform separation, it is possible to increase substantially the information quality providing electrocardiogram classification auxiliary information to doctor.
Wherein the original electrocardiographicdigital figure Wave data of more than 8 seconds has the waveform of abundant amount so that extract the electrocardio obtained
Figure rhythm and pace of moving things information, to represent PQRST Wave data more accurate.The extraction wherein representing PQRST waveform can effectively reduce presumptuously
The waveform change impact that class key element such as human motion, electrode instability are brought, simultaneously because represent the data volume phase of PQRST waveform
A lot of less to the Wave data in original electrocardiographicdigital figure Wave data, its Wave data is more stable, and later stage can be greatly reduced
The training burden of degree of deep learning algorithm, improves the computational efficiency of degree of deep learning algorithm, and is supplied to the electrocardiogram auxiliary of doctor
The quality of classification information.The extraction of electrocardiogram rhythm and pace of moving things information can be used for improving the degree of accuracy of relevant electrocardiogram classification information.
And by electrocardiogram rhythm and pace of moving things information, represent PQRST Wave data, electrocardiogram additional information is arranged in after one-dimensional data by the degree of depth
Practise algorithm and carry out waveform separation, it is possible to allow degree of deep learning algorithm analyze the relatedness between these information, thus improve final
The degree of accuracy of electrocardiogram classification.
As preferably, a kind of based on degree of deep learning algorithm electrocardiogram classification method described above, described step a
In, after obtaining measurement time original electrocardiographicdigital figure Wave data more than 8 seconds, original electrocardiographicdigital figure Wave data is carried out at denoising
Reason.
The baseline drift noise of original electrocardiographicdigital figure Wave data, myoelectricity interference, Hz noise etc. can be removed, thus enter one
Step promotes the accuracy of final electrocardiogram classification result.
As preferably, a kind of based on degree of deep learning algorithm electrocardiogram classification method described above, at described denoising
Reason comprises the following steps:
A11. high pass filter is used to remove baseline drift noise；
A12. standard variance based on PQ segment signal and threshold method confirm that noise is the most too high；
A13. noise uses low pass Butterworth filter to remove noise jamming time too high.
By above step can effectively remove the baseline drift noise in original electrocardiographicdigital figure Wave data, myoelectricity disturb,
Hz noise etc., thus promote the degree of accuracy of final electrocardiogram classification auxiliary information further.
As preferably, a kind of based on degree of deep learning algorithm electrocardiogram classification method described above, described step a
In, described electrocardiogram rhythm and pace of moving things information includes that average ventricular heart rate, average RR-interval, the longest RR interval and the shortest RR are spaced it
The PR that under the standard variance at difference, RR interval, concordance P ripple information, antrum rule, each heart is clapped is spaced and preexcitation wave in meansigma methods, R ripple
Between the QT that under testing result, antrum rule, each heart is clapped, between phase and QTc, phase and meansigma methods, antrum rule QRS average wave width, antrum restrain P ripple
Wide and average wave width, premature contraction information, premature contraction type, premature contraction form, the F ripple of atrial flutter and atrial fibrillation
The testing result of f ripple, the testing result of non-synchronous P ripple.
Information above has considerable influence to final electrocardiogram classification result, it is possible to promote final electrocardiogram further
The degree of accuracy of classification.
As preferably, a kind of based on degree of deep learning algorithm electrocardiogram classification method described above, described step a
In, the extraction representing PQRST waveform comprises the following steps:
A21. by first differential method and threshold method, original electrocardiographicdigital figure Wave data is detected, obtain P ripple, QRS wave, T ripple
Characteristic point；
A22. all PQRST ripples in original electrocardiographicdigital figure Wave data are carried out Cluster Classification, will have according to classification results
The most type of PQRST wave number mesh, as representing PQRST waveform, if the most type of number is more than 2, chooses R popin equal
The maximum type of amplitude as representing PQRST waveform, finally use superposed average method to calculate PQRST ripple that each heart claps average
Waveform is as representing PQRST waveform.
Pass through above step, it is possible to effectively extract the P ripple in original electrocardiographicdigital figure Wave data, QRS wave, the characteristic point of T ripple,
And all PQRST ripples in original electrocardiographicdigital figure Wave data are carried out Cluster Classification, it is possible to effectively remove original electrocardiographicdigital figure waveform
The PQRST waveform that disturbed by noise artifact in data and to and the QRST waveform relevant with the rhythm and pace of moving things, it is ensured that the generation obtained
Table PQRST waveform can transmit effective information more accurately and carry out electrocardiogram classification.
As preferably, a kind of based on degree of deep learning algorithm electrocardiogram classification method described above, described step a
In, described electrocardiogram additional information includes sex, height, chest measurement, body weight, fat percentage, ethnic group.
Information above is relevant with electrocardiogram classification benchmark, final classification results is had considerable influence, it is considered to information above
The degree of accuracy of electrocardiogram classification result can be promoted.
As preferably, a kind of based on degree of deep learning algorithm electrocardiogram classification method described above, the described original heart
Electrograph Wave data is single leads.
Single leads is typically suitable for portable Electrocardiography instrument so that the scope of application of the present invention is wider.
As preferably, a kind of based on degree of deep learning algorithm electrocardiogram classification method described above, in described step a
Original electrocardiographicdigital figure Wave data be multi-lead data, the electrocardiogram rhythm and pace of moving things that described electrocardiogram rhythm and pace of moving things information is led by each letter
Breath is connected into one-dimensional data and is formed, the representative PQRST Wave data series connection that described representative PQRST Wave data is led by each
One-dimensional data is become to be formed.
The original electrocardiographicdigital figure Wave data of multi-lead has more fully information, can promote relevant electrocardiogram classification auxiliary
The degree of accuracy of supplementary information, and the electrocardiogram rhythm and pace of moving things information that the electrocardiogram rhythm and pace of moving things bit string led by each is unified into and being led by each
The representative PQRST Wave data that the representative PQRST Wave data of connection is connected into is carrying out waveform separation by degree of deep learning algorithm
Time, the dependency between each leads can effectively summed up after sufficiently training, it is possible to promotes final further
The degree of accuracy of electrocardiogram classification auxiliary information.
As preferably, a kind of based on degree of deep learning algorithm electrocardiogram classification method described above, described step b
In, described degree of deep learning algorithm is convolutional neural networks or recursive neural network or deep neural network.
Three of the above neutral net has higher accuracy rate, it is possible to ensure the essence of final electrocardiogram classification auxiliary information
Exactness.
Beneficial effects of the present invention is as follows:
1, degree of deep learning algorithm and the conventional ECG sorting algorithm of artificial intelligence field have been carried out rational combination by the present invention,
The degree of accuracy of final electrocardiogram classification auxiliary information can be increased substantially.Present invention utilizes in conventional ECG classification side
The most certified effective information data in method, utilize again the superpower learning capacity of degree of deep learning algorithm, feature automatically to carry simultaneously
Take, the advantageous ability such as feature distribution relation classification automatically inadequate to make up the feature extraction existed in conventional ECG sorting technique
Accurately, dependency classification is insufficient between feature shortcoming, the present invention can learn the substantial amounts of heart automatically by degree of deep learning algorithm
Electrograph Wave data, and sum up sensitive feature and the distribution thereof of each electrocardiogram classification, thus increase substantially the final heart
The degree of accuracy of electrograph classification auxiliary information.
2, the present invention can more effectively provide the auxiliary information of the electrocardiogram classification needed for early treatment Shi doctor.Tradition
Electrocardiogram classification algorithm is a kind of state algorithm, does not have ability of self-teaching, and the present invention is by classifying conventional ECG
Algorithm combines with degree of deep learning algorithm, on the one hand can improve accuracy and the robustness of electrocardiogram classification, on the other hand can carry
Rise the understanding to all kinds of electrocardiogram classification, it is provided that doctor explains the auxiliary information of the mechanism of all kinds of heart disease.
Accompanying drawing explanation
Fig. 1 is the flow chart of a kind of electrocardiogram classification method based on degree of deep learning algorithm of the present invention.
Detailed description of the invention
Below in conjunction with the accompanying drawings 1 and detailed description of the invention the present invention is described in further detail, but they are not to this
Bright restriction:
Embodiment 1
A kind of electrocardiogram classification method based on degree of deep learning algorithm, its flow chart is as it is shown in figure 1, specifically include following steps:
A. (1) obtains single lead electrocardiogram Wave data and electrocardiogram additional information, and according to this list lead electrocardiogram waveform
The data cutout data of the most a length of 10 seconds as original electrocardiographicdigital figure Wave data, wherein single lead electrocardiogram Wave data with
And electrocardiogram additional information can be obtained by existing data base such as European Community's electrocardiogram waveform data storehouse (CSE) or logical
Crossing other approach to obtain, electrocardiogram additional information includes sex, height, chest measurement, body weight, fat percentage, ethnic group.
(2) the original electrocardiographicdigital figure Wave data that as required, can obtain step (1) carries out denoising, denoising
Comprise the following steps:
A11. high pass filter is used to remove baseline drift noise；
A12. standard variance based on PQ segment signal and threshold method confirm that noise is the most too high；
A13. noise uses low pass Butterworth filter to remove noise jamming time too high.
(3) diacritical point of PQRST waveform is calculated according to original electrocardiographicdigital figure Wave data, thus according to the district of PQRST waveform
Branch carries out the extraction of electrocardiogram rhythm and pace of moving things information, obtains electrocardiogram rhythm and pace of moving things information, and wherein electrocardiogram rhythm and pace of moving things information includes the average heart
Room heart rate, average RR-interval, the longest RR interval and the shortest RR interval difference, RR interval standard variance, concordance P ripple information,
Antrum rule under each heart clap PR interval and meansigma methods, R ripple in preexcitation wave testing result, antrum rule under each heart clap QT between the phase and
Phase and meansigma methods, antrum rule QRS average wave width, antrum rule P ripple width and average wave width, premature contraction information, premature contraction between QTc
Type, premature contraction form, the F ripple of atrial flutter and the testing result of f ripple of atrial fibrillation, the testing result of non-synchronous P ripple,
And represent PQRST waveform and extracted by following steps:
A21. by first differential method and threshold method, original electrocardiographicdigital figure Wave data is detected, obtain P ripple, QRS wave, T ripple
Characteristic point；
A22. all PQRST ripples in original electrocardiographicdigital figure Wave data are carried out Cluster Classification, will have according to classification results
The most type of PQRST wave number mesh, as representing PQRST waveform, if the most type of number is more than 2, chooses R popin equal
The maximum type of amplitude as representing PQRST waveform class, finally use superposed average method to calculate PQRST ripple that each heart claps flat
All waveforms are as representing PQRST waveform.
(4) in order to convolutional neural networks is trained, in addition it is also necessary to obtaining training data, these training data can be from it
Its corresponding physical examination result obtains, it is also possible to select from existing data base such as from European Community's electrocardiogram waveform data storehouse
(CSE) obtaining in, wherein training data include other corresponding single lead electrocardiogram Wave data and electrocardiogram additional information,
As a example by European Community's electrocardiogram waveform data storehouse (CSE), the obtaining step of training data is: according to step (1)-step (3)
Each information in European Community's electrocardiogram waveform data storehouse (CSE) is processed, obtains European Community's electrocardiogram waveform data storehouse
(CSE) each electrocardiogram rhythm and pace of moving things information in, PQRST Wave data, electrocardiogram additional information are represented.
B. (1) sets convolutional neural networks input layer, hidden layer, the node number of output layer, and sets adjacent layer at random
Weight between each node.
(2) the electrocardiogram rhythm and pace of moving things information in the training data that the step (4) in step a is obtained, represent PQRST waveform
Data, electrocardiogram additional information are arranged in after one-dimensional data and input from the input of convolutional neural networks, and corresponding true
Electrocardiogram classification result is input to the result end of convolutional neural networks and carrys out training convolutional neural networks.
(3) after completing the training to convolutional neural networks, by the electrocardiogram additional information in the step (1) of step a, step
The step (3) of rapid a represents PQRST Wave data and electrocardiogram rhythm and pace of moving things information arrangement become one-dimensional data after be input to convolution god
Through the input of network, electrocardiogram classification result, wherein electrocardio can be obtained after carrying out waveform separation by convolutional neural networks
Figure additional information, represent PQRST Wave data, the arrangement mode of electrocardiogram rhythm and pace of moving things information can select according to practical situation.
Embodiment 2
A kind of electrocardiogram classification method based on degree of deep learning algorithm, its flow chart is as it is shown in figure 1, specifically include following steps:
A. (1) obtains single lead electrocardiogram Wave data and electrocardiogram additional information, and according to this list lead electrocardiogram waveform
The data cutout data of the most a length of 8 seconds as original electrocardiographicdigital figure Wave data, wherein single lead electrocardiogram Wave data with
And electrocardiogram additional information can be obtained by existing data base such as European Community's electrocardiogram waveform data storehouse (CSE) or logical
Crossing other approach to obtain, electrocardiogram additional information includes sex, height, chest measurement, body weight, fat percentage, ethnic group.
(2) the original electrocardiographicdigital figure Wave data that as required, can obtain step (1) carries out denoising, denoising
Comprise the following steps:
A11. high pass filter is used to remove baseline drift noise；
A12. standard variance based on PQ segment signal and threshold method confirm that noise is the most too high；
A13. noise uses low pass Butterworth filter to remove noise jamming time too high.
(3) diacritical point of PQRST waveform is calculated according to original electrocardiographicdigital figure Wave data, thus according to the district of PQRST waveform
Branch carries out the extraction of electrocardiogram rhythm and pace of moving things information, obtains electrocardiogram rhythm and pace of moving things information, and wherein electrocardiogram rhythm and pace of moving things information includes the average heart
Room heart rate, average RR-interval, the longest RR interval and the shortest RR interval difference, RR interval standard variance, concordance P ripple information,
Antrum rule under each heart clap PR interval and meansigma methods, R ripple in preexcitation wave testing result, antrum rule under each heart clap QT between the phase and
Phase and meansigma methods, antrum rule QRS average wave width, antrum rule P ripple width and average wave width, premature contraction information, premature contraction between QTc
Type, premature contraction form, the F ripple of atrial flutter and the testing result of f ripple of atrial fibrillation, the testing result of non-synchronous P ripple,
And represent PQRST waveform and extracted by following steps:
A21. by first differential method and threshold method, original electrocardiographicdigital figure Wave data is detected, obtain P ripple, QRS wave, T ripple
Characteristic point；
A22. all PQRST ripples in original electrocardiographicdigital figure Wave data are carried out Cluster Classification, will have according to classification results
The most type of PQRST wave number mesh, as representing PQRST waveform, if the most type of number is more than 2, chooses R popin equal
The maximum type of amplitude as representing PQRST waveform class, finally use superposed average method to calculate PQRST ripple that each heart claps flat
All waveforms are as representing PQRST waveform.
(4) in order to recursive neural network is trained, in addition it is also necessary to obtaining training data, these training data can be selected
Selecting and such as obtain from European Community's electrocardiogram waveform data storehouse (CSE) from existing data base, wherein training data include it
Its corresponding coverlet lead electrocardiogram Wave data and electrocardiogram additional information, with European Community's electrocardiogram waveform data storehouse (CSE)
As a example by, the obtaining step of training data is: according to step (1)-step (3) to European Community's electrocardiogram waveform data storehouse (CSE)
In each information process, obtain each electrocardiogram rhythm and pace of moving things information, representative in European Community's electrocardiogram waveform data storehouse (CSE)
PQRST Wave data, electrocardiogram additional information.
B. (1) sets recursive neural network input layer, hidden layer, the node number of output layer, and sets adjacent layer at random
Weight between each node.
(2) the electrocardiogram rhythm and pace of moving things information in the training data that the step (4) in step a is obtained, represent PQRST waveform
Data, electrocardiogram additional information are arranged in after one-dimensional data and input from the input of recursive neural network, and corresponding true
Classification results is input to the result end of recursive neural network to train recursive neural network.
(3) after completing the training to recursive neural network, by the electrocardiogram additional information in the step (1) of step a, step
The step (3) of rapid a represents PQRST Wave data and electrocardiogram rhythm and pace of moving things information arrangement become one-dimensional data after be input to iteration god
Through the input of network, electrocardiogram classification result, wherein electrocardio can be obtained after carrying out waveform separation by recursive neural network
Figure additional information, represent PQRST Wave data, the arrangement mode of electrocardiogram rhythm and pace of moving things information can select according to practical situation.
Embodiment 3
A kind of electrocardiogram classification method based on degree of deep learning algorithm, its flow chart is as it is shown in figure 1, specifically include following steps:
A. (1) obtains single lead electrocardiogram Wave data and electrocardiogram additional information, and according to this list lead electrocardiogram waveform
The data cutout data of the most a length of 16 seconds as original electrocardiographicdigital figure Wave data, wherein single lead electrocardiogram Wave data with
And electrocardiogram additional information can be obtained by existing data base such as European Community's electrocardiogram waveform data storehouse (CSE) or logical
Crossing other approach to obtain, electrocardiogram additional information includes sex, height, chest measurement, body weight, fat percentage, ethnic group.
(2) the original electrocardiographicdigital figure Wave data that as required, can obtain step (1) carries out denoising, denoising
Comprise the following steps:
A11. high pass filter is used to remove baseline drift noise；
A12. standard variance based on PQ segment signal and threshold method confirm that noise is the most too high；
A13. noise uses low pass Butterworth filter to remove noise jamming time too high.
(3) diacritical point of PQRST waveform is calculated according to original electrocardiographicdigital figure Wave data, thus according to the district of PQRST waveform
Branch carries out the extraction of electrocardiogram rhythm and pace of moving things information, obtains electrocardiogram rhythm and pace of moving things information, and wherein electrocardiogram rhythm and pace of moving things information includes the average heart
Room heart rate, average RR-interval, the longest RR interval and the shortest RR interval difference, RR interval standard variance, concordance P ripple information,
Antrum rule under each heart clap PR interval and meansigma methods, R ripple in preexcitation wave testing result, antrum rule under each heart clap QT between the phase and
Phase and meansigma methods, antrum rule QRS average wave width, antrum rule P ripple width and average wave width, premature contraction information, premature contraction between QTc
Type, premature contraction form, the F ripple of atrial flutter and the testing result of f ripple of atrial fibrillation, the testing result of non-synchronous P ripple,
And represent PQRST waveform and extracted by following steps:
A21. by first differential method and threshold method, original electrocardiographicdigital figure Wave data is detected, obtain P ripple, QRS wave, T ripple
Characteristic point；
A22. all PQRST ripples in original electrocardiographicdigital figure Wave data are carried out Cluster Classification, will have according to classification results
The most type of PQRST wave number mesh, as representing PQRST waveform, if the most type of number is more than 2, chooses R popin equal
The maximum type of amplitude as representing PQRST waveform class, finally use superposed average method to calculate PQRST ripple that each heart claps flat
All waveforms are as representing PQRST waveform.
(4) in order to deep neural network is trained, in addition it is also necessary to obtaining training data, these training data can be from it
Its corresponding physical examination result obtains, it is also possible to select from existing data base such as from European Community's electrocardiogram waveform data storehouse
(CSE) obtaining in, wherein training data include other corresponding single lead electrocardiogram Wave data and electrocardiogram additional information,
As a example by European Community's electrocardiogram waveform data storehouse (CSE), the obtaining step of training data is: according to step (1)-step (3)
Each information in European Community's electrocardiogram waveform data storehouse (CSE) is processed, obtains European Community's electrocardiogram waveform data storehouse
(CSE) each electrocardiogram rhythm and pace of moving things information in, PQRST Wave data, electrocardiogram additional information are represented.
B. the node number of (1) set depth neural network input layer, hidden layer, output layer, and set adjacent layer at random
Weight between each node.
(2) the electrocardiogram rhythm and pace of moving things information in the training data that the step (4) in step a is obtained, represent PQRST waveform
Data, electrocardiogram additional information are arranged in after one-dimensional data and input from the input of deep neural network, and corresponding true
Electrocardiogram classification result is input to the result end of deep neural network to train deep neural network.
(3) after completing the training to deep neural network, by the electrocardiogram additional information in the step (1) of step a, step
The step (3) of rapid a represents PQRST Wave data and electrocardiogram rhythm and pace of moving things information arrangement become one-dimensional data after be input to degree of depth god
Through the input of network, electrocardiogram classification result, wherein electrocardio can be obtained after carrying out waveform separation by deep neural network
Figure additional information, represent PQRST Wave data, the arrangement mode of electrocardiogram rhythm and pace of moving things information can select according to practical situation.
Embodiment 4
A kind of electrocardiogram classification method based on degree of deep learning algorithm, its flow chart is as it is shown in figure 1, specifically include following steps:
A. (1) obtains multi-lead electrocardiogram waveform data and electrocardiogram additional information, and according to this multi-lead electrocardiographic wave
The data cutout data of the most a length of 10 seconds as original electrocardiographicdigital figure Wave data, wherein multi-lead electrocardiogram waveform data with
And electrocardiogram additional information can, it is also possible to obtained by existing data base such as European Community's electrocardiogram waveform data storehouse (CSE), or
Person is obtained by other approach, and this multi-lead electrocardiogram waveform data can be twelve-lead electrocardiogram Wave data, three lead
The multi-lead electrocardiographic waves such as electrocardiogram waveform data, six lead electrocardiogram Wave datas, 18 lead electrocardiogram Wave datas
Data.Electrocardiogram additional information includes sex, height, chest measurement, body weight, fat percentage, ethnic group.
(2) the original electrocardiographicdigital figure Wave data that as required, can obtain step (1) carries out denoising, denoising
Comprise the following steps:
A11. high pass filter is used to remove baseline drift noise；
A12. standard variance based on PQ segment signal and threshold method confirm that noise is the most too high；
A13. noise uses low pass Butterworth filter to remove noise jamming time too high.
(3) diacritical point of PQRST waveform is calculated according to original electrocardiographicdigital figure Wave data, thus according to the district of PQRST waveform
Branch carries out the extraction of electrocardiogram rhythm and pace of moving things information, obtains electrocardiogram rhythm and pace of moving things information, and wherein electrocardiogram rhythm and pace of moving things information includes the average heart
Room heart rate, average RR-interval, the longest RR interval and the shortest RR interval difference, RR interval standard variance, concordance P ripple information,
Antrum rule under each heart clap PR interval and meansigma methods, R ripple in preexcitation wave testing result, antrum rule under each heart clap QT between the phase and
Phase and meansigma methods, antrum rule QRS average wave width, antrum rule P ripple width and average wave width, premature contraction information, premature contraction between QTc
Type, premature contraction form, the F ripple of atrial flutter and the testing result of f ripple of atrial fibrillation, the testing result of non-synchronous P ripple,
And represent PQRST waveform and extracted by following steps:
A21. by first differential method and threshold method, original electrocardiographicdigital figure Wave data is detected, obtain P ripple, QRS wave, T ripple
Characteristic point；
A22. all PQRST ripples in original electrocardiographicdigital figure Wave data are carried out Cluster Classification, will have according to classification results
The most type of PQRST wave number mesh, as representing PQRST waveform, if the most type of number is more than 2, chooses R popin equal
The maximum type of amplitude as representing PQRST waveform class, finally use superposed average method to calculate PQRST ripple that each heart claps flat
All waveforms are as representing PQRST waveform.
(4) in order to convolutional neural networks is trained, in addition it is also necessary to obtaining training data, these training data can be from choosing
Selecting and such as obtain from European Community's electrocardiogram waveform data storehouse (CSE) from existing data base, wherein training data include it
Its corresponding multi-lead electrocardiogram waveform data and electrocardiogram additional information, with European Community's electrocardiogram waveform data storehouse (CSE) be
Example, the obtaining step of training data is: according to step (1)-step (3) in European Community's electrocardiogram waveform data storehouse (CSE)
Each information processes, and obtains each electrocardiogram rhythm and pace of moving things information, representative in European Community's electrocardiogram waveform data storehouse (CSE)
PQRST Wave data, electrocardiogram additional information.
B. (1) sets convolutional neural networks input layer, hidden layer, the node number of output layer, and sets adjacent layer at random
Weight between each node.
(2) the electrocardiogram rhythm and pace of moving things information in the training data that the step (4) in step a is obtained, represent PQRST waveform
Data, electrocardiogram additional information are arranged in after one-dimensional data and input from the input of convolutional neural networks, and corresponding true
Electrocardiogram classification result is input to the result end of convolutional neural networks and carrys out training convolutional neural networks.
(3) after completing the training to convolutional neural networks, by the electrocardiogram additional information in the step (1) of step a, step
The step (3) of rapid a represents PQRST Wave data and electrocardiogram rhythm and pace of moving things information arrangement become one-dimensional data after be input to convolution god
Through the input of network, electrocardiogram classification result, wherein electrocardio can be obtained after carrying out waveform separation by convolutional neural networks
Figure additional information, represent PQRST Wave data, the arrangement mode of electrocardiogram rhythm and pace of moving things information can select according to practical situation.
Embodiment 5
A kind of electrocardiogram classification method based on degree of deep learning algorithm, its flow chart is as it is shown in figure 1, specifically include following steps:
A. (1) obtains multi-lead electrocardiogram waveform data and electrocardiogram additional information, and according to this multi-lead electrocardiographic wave
The data cutout data of the most a length of 8 seconds as original electrocardiographicdigital figure Wave data, wherein multi-lead electrocardiogram waveform data with
And electrocardiogram additional information can, it is also possible to obtained by existing data base such as European Community's electrocardiogram waveform data storehouse (CSE), or
Person is obtained by other approach, and this multi-lead electrocardiogram waveform data can be twelve-lead electrocardiogram Wave data, three lead
The multi-lead electrocardiographic waves such as electrocardiogram waveform data, six lead electrocardiogram Wave datas, 18 lead electrocardiogram Wave datas
Data.Electrocardiogram additional information includes sex, height, chest measurement, body weight, fat percentage, ethnic group.
(2) the original electrocardiographicdigital figure Wave data that as required, can obtain step (1) carries out denoising, denoising
Comprise the following steps:
A11. high pass filter is used to remove baseline drift noise；
A12. standard variance based on PQ segment signal and threshold method confirm that noise is the most too high；
A13. noise uses low pass Butterworth filter to remove noise jamming time too high.
(3) diacritical point of PQRST waveform is calculated according to original electrocardiographicdigital figure Wave data, thus according to the district of PQRST waveform
Branch carries out the extraction of electrocardiogram rhythm and pace of moving things information, obtains electrocardiogram rhythm and pace of moving things information, and wherein electrocardiogram rhythm and pace of moving things information includes the average heart
Room heart rate, average RR-interval, the longest RR interval and the shortest RR interval difference, RR interval standard variance, concordance P ripple information,
Antrum rule under each heart clap PR interval and meansigma methods, R ripple in preexcitation wave testing result, antrum rule under each heart clap QT between the phase and
Phase and meansigma methods, antrum rule QRS average wave width, antrum rule P ripple width and average wave width, premature contraction information, premature contraction between QTc
Type, premature contraction form, the F ripple of atrial flutter and the testing result of f ripple of atrial fibrillation, the testing result of non-synchronous P ripple,
And represent PQRST waveform and extracted by following steps:
A21. by first differential method and threshold method, original electrocardiographicdigital figure Wave data is detected, obtain P ripple, QRS wave, T ripple
Characteristic point；
A22. all PQRST ripples in original electrocardiographicdigital figure Wave data are carried out Cluster Classification, will have according to classification results
The most type of PQRST wave number mesh, as representing PQRST waveform, if the most type of number is more than 2, chooses R popin equal
The maximum type of amplitude as representing PQRST waveform class, finally use superposed average method to calculate PQRST ripple that each heart claps flat
All waveforms are as representing PQRST waveform.
(4) in order to recursive neural network is trained, in addition it is also necessary to obtaining training data, these training data can be from it
Its corresponding physical examination result obtains, it is also possible to select from existing data base such as from European Community's electrocardiogram waveform data storehouse
(CSE) obtaining in, wherein training data include other corresponding multi-lead electrocardiogram waveform data and electrocardiogram additional information,
As a example by European Community's electrocardiogram waveform data storehouse (CSE), the obtaining step of training data is: according to step (1)-step (3)
Each information in European Community's electrocardiogram waveform data storehouse (CSE) is processed, obtains European Community's electrocardiogram waveform data storehouse
(CSE) each electrocardiogram rhythm and pace of moving things information in, PQRST Wave data, electrocardiogram additional information are represented.
B. (1) sets recursive neural network input layer, hidden layer, the node number of output layer, and sets adjacent layer at random
Weight between each node.
(2) the electrocardiogram rhythm and pace of moving things information in the training data that the step (4) in step a is obtained, represent PQRST waveform
Data, electrocardiogram additional information are arranged in after one-dimensional data and input from the input of recursive neural network, and corresponding true
Electrocardiogram classification result is input to the result end of recursive neural network to train recursive neural network.
(3) after completing the training to recursive neural network, by the electrocardiogram additional information in the step (1) of step a, step
The step (3) of rapid a represents PQRST Wave data and electrocardiogram rhythm and pace of moving things information arrangement become one-dimensional data after be input to iteration god
Through the input of network, electrocardiogram classification result, wherein electrocardio can be obtained after carrying out waveform separation by recursive neural network
Figure additional information, represent PQRST Wave data, the arrangement mode of electrocardiogram rhythm and pace of moving things information can select according to practical situation.
Embodiment 6
A kind of electrocardiogram classification method based on degree of deep learning algorithm, its flow chart is as it is shown in figure 1, specifically include following steps:
A. (1) obtains multi-lead electrocardiogram waveform data and electrocardiogram additional information, and according to this multi-lead electrocardiographic wave
The data cutout data of the most a length of 16 seconds as original electrocardiographicdigital figure Wave data, wherein multi-lead electrocardiogram waveform data with
And electrocardiogram additional information can, it is also possible to obtained by existing data base such as European Community's electrocardiogram waveform data storehouse (CSE), or
Person is obtained by other approach, and this multi-lead electrocardiogram waveform data can be twelve-lead electrocardiogram Wave data, three lead
The multi-lead electrocardiographic waves such as electrocardiogram waveform data, six lead electrocardiogram Wave datas, 18 lead electrocardiogram Wave datas
Data.Electrocardiogram additional information includes sex, height, chest measurement, body weight, fat percentage, ethnic group.
(2) the original electrocardiographicdigital figure Wave data that as required, can obtain step (1) carries out denoising, denoising
Comprise the following steps:
A11. high pass filter is used to remove baseline drift noise；
A12. standard variance based on PQ segment signal and threshold method confirm that noise is the most too high；
A13. noise uses low pass Butterworth filter to remove noise jamming time too high.
(3) diacritical point of PQRST waveform is calculated according to original electrocardiographicdigital figure Wave data, thus according to the district of PQRST waveform
Branch carries out the extraction of electrocardiogram rhythm and pace of moving things information, obtains electrocardiogram rhythm and pace of moving things information, and wherein electrocardiogram rhythm and pace of moving things information includes the average heart
Room heart rate, average RR-interval, the longest RR interval and the shortest RR interval difference, RR interval standard variance, concordance P ripple information,
Antrum rule under each heart clap PR interval and meansigma methods, R ripple in preexcitation wave testing result, antrum rule under each heart clap QT between the phase and
Phase and meansigma methods, antrum rule QRS average wave width, antrum rule P ripple width and average wave width, premature contraction information, premature contraction between QTc
Type, premature contraction form, the F ripple of atrial flutter and the testing result of f ripple of atrial fibrillation, the testing result of non-synchronous P ripple,
And represent PQRST waveform and extracted by following steps:
A21. by first differential method and threshold method, original electrocardiographicdigital figure Wave data is detected, obtain P ripple, QRS wave, T ripple
Characteristic point；
A22. all PQRST ripples in original electrocardiographicdigital figure Wave data are carried out Cluster Classification, will have according to classification results
The most type of PQRST wave number mesh, as representing PQRST waveform, if the most type of number is more than 2, chooses R popin equal
The maximum type of amplitude as representing PQRST waveform class, finally use superposed average method to calculate PQRST ripple that each heart claps flat
All waveforms are as representing PQRST waveform.
(4) in order to deep neural network is trained, in addition it is also necessary to obtaining training data, these training data can be from it
Its corresponding physical examination result obtains, it is also possible to select from existing data base such as from European Community's electrocardiogram waveform data storehouse
(CSE) obtaining in, wherein training data include other corresponding multi-lead electrocardiogram waveform data and electrocardiogram additional information,
As a example by European Community's electrocardiogram waveform data storehouse (CSE), the obtaining step of training data is: according to step (1)-step (3)
Each information in European Community's electrocardiogram waveform data storehouse (CSE) is processed, obtains European Community's electrocardiogram waveform data storehouse
(CSE) each electrocardiogram rhythm and pace of moving things information in, PQRST Wave data, electrocardiogram additional information are represented.
B. the node number of (1) set depth neural network input layer, hidden layer, output layer, and set adjacent layer at random
Weight between each node.
(2) the electrocardiogram rhythm and pace of moving things information in the training data that the step (4) in step a is obtained, represent PQRST waveform
Data, electrocardiogram additional information are arranged in after one-dimensional data and input from the input of deep neural network, and corresponding true
Electrocardiogram classification result is input to the result end of deep neural network to train deep neural network.
(3) after completing the training to deep neural network, by the electrocardiogram additional information in the step (1) of step a, step
The step (3) of rapid a represents PQRST Wave data and electrocardiogram rhythm and pace of moving things information arrangement become one-dimensional data after be input to degree of depth god
Through the input of network, electrocardiogram classification result, wherein electrocardio can be obtained after carrying out waveform separation by deep neural network
Figure additional information, represent PQRST Wave data, the arrangement mode of electrocardiogram rhythm and pace of moving things information can select according to practical situation.
Embodiment 7
The present embodiment by the electrocardiogram classification result of the electrocardiogram classification result of embodiment 1 to embodiment 6 and traditional measurement from
Sensitivity, specificity these two aspects compare, and comparative result is as shown in the table:
As can be known from the above table, the electrocardiogram classification result obtained by the present invention, its sensitivity, specificity are compared traditional measurement and are obtained
To electrocardiogram classification result improve about 10%, substantially remain in about 97%, it is possible to meet actual proposing to doctor well
The needs of the electrocardiogram classification information required for diagnosing for auxiliary.
In a word, the foregoing is only presently preferred embodiments of the present invention, all according to the scope of the present patent application patent made equal
Deng change and modification, the covering scope of the present invention all should be belonged to.
Claims (9)
1. an electrocardiogram classification method based on degree of deep learning algorithm, it is characterised in that comprise the following steps:
A. measurement time original electrocardiographicdigital figure Wave data more than 8 seconds, electrocardiogram additional information are obtained, and according to original electrocardiographicdigital
Figure Wave data carries out the extraction of electrocardiogram rhythm and pace of moving things information, represents the extraction of PQRST waveform, obtains electrocardiogram rhythm and pace of moving things information, generation
Table PQRST Wave data；
B. the neutral net of degree of deep learning algorithm is trained, the electrocardiogram rhythm and pace of moving things information that step a obtained, represents PQRST
Wave data, electrocardiogram additional information carry out waveform separation by the degree of deep learning algorithm trained after being arranged in one-dimensional data,
Obtain electrocardiogram classification result.
A kind of electrocardiogram classification method based on degree of deep learning algorithm the most according to claim 1, it is characterised in that: described
Step a in, obtain after measurement time original electrocardiographicdigital figure Wave data more than 8 seconds, original electrocardiographicdigital figure Wave data entered
Row denoising.
A kind of electrocardiogram classification method based on degree of deep learning algorithm the most according to claim 2, it is characterised in that: described
Denoising comprise the following steps:
A11. high pass filter is used to remove baseline drift noise；
A12. standard variance based on PQ segment signal and threshold method confirm that noise is the most too high；
A13. noise uses low pass Butterworth filter to remove noise jamming time too high.
A kind of electrocardiogram classification method based on degree of deep learning algorithm the most according to claim 1, it is characterised in that: described
Step a in, described electrocardiogram rhythm and pace of moving things information include average ventricular heart rate, average RR-interval, the longest RR interval and the shortest RR
The PR that under the standard variance at the difference at interval, RR interval, concordance P ripple information, antrum rule, each heart is clapped is spaced and in meansigma methods, R ripple
Between the QT that under preexcitation wave testing result, antrum rule, each heart is clapped, between phase and QTc, phase and meansigma methods, antrum restrain QRS average wave width, antrum
Rule P ripple width and average wave width, premature contraction information, premature contraction type, premature contraction form, the F ripple of atrial flutter and atrium
The testing result of f ripple of vibration, the testing result of non-synchronous P ripple.
A kind of electrocardiogram classification method based on degree of deep learning algorithm the most according to claim 1, it is characterised in that: described
Step a in, the extraction representing PQRST waveform comprises the following steps:
A21. by first differential method and threshold method, original electrocardiographicdigital figure Wave data is detected, obtain P ripple, QRS wave, T ripple
Characteristic point；
A22. all PQRST ripples in original electrocardiographicdigital figure Wave data are carried out Cluster Classification, will have according to classification results
The most type of PQRST wave number mesh, as representing PQRST waveform, if the most type of number is more than 2, chooses R popin equal
The maximum type of amplitude as representing PQRST waveform, finally use superposed average method to calculate PQRST ripple that each heart claps average
Waveform is as representing PQRST waveform.
A kind of electrocardiogram classification method based on degree of deep learning algorithm the most according to claim 1, it is characterised in that: described
Step a in, described electrocardiogram additional information includes sex, height, chest measurement, body weight, fat percentage, ethnic group.
A kind of electrocardiogram classification method based on degree of deep learning algorithm the most according to claim 1, it is characterised in that: described
Original electrocardiographicdigital figure Wave data be single leads.
A kind of electrocardiogram classification method based on degree of deep learning algorithm the most according to claim 1, it is characterised in that: described
Step a in original electrocardiographicdigital figure Wave data be multi-lead data, the heart that described electrocardiogram rhythm and pace of moving things information is led by each
Electrograph rhythm and pace of moving things bit string is unified into one-dimensional data and is formed, the representative PQRST ripple that described representative PQRST Wave data is led by each
Graphic data is connected into one-dimensional data and is formed.
9. according to a kind of based on degree of deep learning algorithm the electrocardiogram classification side described in any claim in claim 1 to 8
Method, it is characterised in that: in described step b, described degree of deep learning algorithm be convolutional neural networks or recursive neural network or
Deep neural network.
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Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106778685A (en) * | 2017-01-12 | 2017-05-31 | 司马大大（北京）智能系统有限公司 | Electrocardiogram image-recognizing method, device and service terminal |
| CN107203692A (en) * | 2017-05-09 | 2017-09-26 | 哈尔滨工业大学（威海） | The implementation method of atrial fibrillation detection based on depth convolutional neural networks |
| CN107256393A (en) * | 2017-06-05 | 2017-10-17 | 四川大学 | The feature extraction and state recognition of one-dimensional physiological signal based on deep learning |
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| CN107837082A (en) * | 2017-11-27 | 2018-03-27 | 乐普（北京）医疗器械股份有限公司 | Electrocardiogram automatic analysis method and device based on artificial intelligence self study |
| CN107981858A (en) * | 2017-11-27 | 2018-05-04 | 乐普（北京）医疗器械股份有限公司 | Electrocardiogram heartbeat automatic recognition classification method based on artificial intelligence |
| CN108399369A (en) * | 2018-02-02 | 2018-08-14 | 东南大学 | Electrocardio beat sorting technique based on Distributed Calculation and deep learning |
| CN108511055A (en) * | 2017-02-27 | 2018-09-07 | 中国科学院苏州纳米技术与纳米仿生研究所 | Ventricular premature beat identifying system and method based on Multiple Classifier Fusion and diagnostic rule |
| CN108509823A (en) * | 2017-02-24 | 2018-09-07 | 深圳市理邦精密仪器股份有限公司 | The detection method and device of QRS complex |
| CN108836302A (en) * | 2018-03-19 | 2018-11-20 | 武汉海星通技术股份有限公司 | Electrocardiogram intelligent analysis method and system based on deep neural network |
| CN108960050A (en) * | 2018-05-25 | 2018-12-07 | 东软集团股份有限公司 | Classification model training method, ECG data classifying method, device and equipment |
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| CN109480825A (en) * | 2018-12-13 | 2019-03-19 | 武汉中旗生物医疗电子有限公司 | The processing method and processing device of electrocardiogram (ECG) data |
| CN109620210A (en) * | 2019-01-28 | 2019-04-16 | 山东科技大学 | A kind of electrocardiosignal classification method of the CNN based on from coding mode in conjunction with GRU |
| CN109770861A (en) * | 2019-03-29 | 2019-05-21 | 广州视源电子科技股份有限公司 | Method, device, equipment and storage medium for training and detecting cardioelectric rhythm model |
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| CN111110224A (en) * | 2020-01-17 | 2020-05-08 | 武汉中旗生物医疗电子有限公司 | Electrocardiogram classification method and device based on multi-angle feature extraction |
| CN111297350A (en) * | 2020-02-27 | 2020-06-19 | 福州大学 | Three-heart beat multi-model comprehensive decision-making electrocardiogram feature classification method integrating source end influence |
| CN111557660A (en) * | 2020-06-08 | 2020-08-21 | 东北大学 | Arrhythmia identification method under sub-population deep learning framework |
| RU2823433C1 (en) * | 2023-11-21 | 2024-07-23 | Общество С Ограниченной Ответственностью "Компания "Стрим Лабс" | Method of processing and analysing electrocardiogram (ecg) data |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100125184A1 (en) * | 2008-11-19 | 2010-05-20 | National Yang Ming University | Chip for sensing a physiological signal and method for sensing the same |
| CN101766484A (en) * | 2010-01-18 | 2010-07-07 | 董军 | Method and equipment for identification and classification of electrocardiogram |
| CN101969842A (en) * | 2008-01-14 | 2011-02-09 | 皇家飞利浦电子股份有限公司 | Atrial fibrillation monitoring |
| CN102028460A (en) * | 2011-01-04 | 2011-04-27 | 复旦大学 | Ventricular fibrillation signal sequence automatic-detection system |
| CN102908135A (en) * | 2012-10-08 | 2013-02-06 | 中国科学院深圳先进技术研究院 | ECG diagnosis system and operating method of ECG diagnosis system |
| CN103038772A (en) * | 2010-03-15 | 2013-04-10 | 新加坡保健服务集团有限公司 | Method of predicting the survivability of a patient |
| CN104873186A (en) * | 2015-04-17 | 2015-09-02 | 中国科学院苏州生物医学工程技术研究所 | Wearable artery detection device and data processing method thereof |
-
2016
- 2016-07-20 CN CN201610572216.4A patent/CN106214145B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101969842A (en) * | 2008-01-14 | 2011-02-09 | 皇家飞利浦电子股份有限公司 | Atrial fibrillation monitoring |
| US20100125184A1 (en) * | 2008-11-19 | 2010-05-20 | National Yang Ming University | Chip for sensing a physiological signal and method for sensing the same |
| CN101766484A (en) * | 2010-01-18 | 2010-07-07 | 董军 | Method and equipment for identification and classification of electrocardiogram |
| CN103038772A (en) * | 2010-03-15 | 2013-04-10 | 新加坡保健服务集团有限公司 | Method of predicting the survivability of a patient |
| CN102028460A (en) * | 2011-01-04 | 2011-04-27 | 复旦大学 | Ventricular fibrillation signal sequence automatic-detection system |
| CN102908135A (en) * | 2012-10-08 | 2013-02-06 | 中国科学院深圳先进技术研究院 | ECG diagnosis system and operating method of ECG diagnosis system |
| CN104873186A (en) * | 2015-04-17 | 2015-09-02 | 中国科学院苏州生物医学工程技术研究所 | Wearable artery detection device and data processing method thereof |
Non-Patent Citations (1)
| Title |
|---|
| 闵洁: "小波变换动态心电图波形特征聚类分析", 《中国卫生产业》 * |
Cited By (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN107256393A (en) * | 2017-06-05 | 2017-10-17 | 四川大学 | The feature extraction and state recognition of one-dimensional physiological signal based on deep learning |
| US11564612B2 (en) | 2017-11-27 | 2023-01-31 | Shanghai Lepu CloudMed Co., LTD | Automatic recognition and classification method for electrocardiogram heartbeat based on artificial intelligence |
| CN107714023A (en) * | 2017-11-27 | 2018-02-23 | 乐普（北京）医疗器械股份有限公司 | Static ecg analysis method and apparatus based on artificial intelligence self study |
| US11344243B2 (en) | 2017-11-27 | 2022-05-31 | Shanghai Lepu CloudMed Co., LTD | Artificial intelligence self-learning-based static electrocardiography analysis method and apparatus |
| CN107981858A (en) * | 2017-11-27 | 2018-05-04 | 乐普（北京）医疗器械股份有限公司 | Electrocardiogram heartbeat automatic recognition classification method based on artificial intelligence |
| CN107837082A (en) * | 2017-11-27 | 2018-03-27 | 乐普（北京）医疗器械股份有限公司 | Electrocardiogram automatic analysis method and device based on artificial intelligence self study |
| CN107714023B (en) * | 2017-11-27 | 2020-09-01 | 上海优加利健康管理有限公司 | Static electrocardiogram analysis method and device based on artificial intelligence self-learning |
| WO2019100566A1 (en) * | 2017-11-27 | 2019-05-31 | 乐普（北京）医疗器械股份有限公司 | Artificial intelligence self-learning-based static electrocardiography analysis method and apparatus |
| CN108399369A (en) * | 2018-02-02 | 2018-08-14 | 东南大学 | Electrocardio beat sorting technique based on Distributed Calculation and deep learning |
| CN108399369B (en) * | 2018-02-02 | 2021-10-19 | 东南大学 | Electrocardio beat classification method based on distributed computation and deep learning |
| CN108836302B (en) * | 2018-03-19 | 2021-06-04 | 武汉海星通技术股份有限公司 | Intelligent electrocardiogram analysis method and system based on deep neural network |
| CN108836302A (en) * | 2018-03-19 | 2018-11-20 | 武汉海星通技术股份有限公司 | Electrocardiogram intelligent analysis method and system based on deep neural network |
| WO2019200746A1 (en) * | 2018-04-20 | 2019-10-24 | 平安科技（深圳）有限公司 | Ecg signal detection method, device, computer apparatus, and storage medium |
| CN108960050A (en) * | 2018-05-25 | 2018-12-07 | 东软集团股份有限公司 | Classification model training method, ECG data classifying method, device and equipment |
| CN109077721B (en) * | 2018-07-20 | 2021-03-23 | 广州视源电子科技股份有限公司 | Atrial fibrillation detection apparatus and storage medium |
| CN109077721A (en) * | 2018-07-20 | 2018-12-25 | 广州视源电子科技股份有限公司 | Atrial fibrillation detection apparatus and storage medium |
| CN109480825A (en) * | 2018-12-13 | 2019-03-19 | 武汉中旗生物医疗电子有限公司 | The processing method and processing device of electrocardiogram (ECG) data |
| CN109480825B (en) * | 2018-12-13 | 2021-08-06 | 武汉中旗生物医疗电子有限公司 | Electrocardio data processing method and device |
| CN109620210A (en) * | 2019-01-28 | 2019-04-16 | 山东科技大学 | A kind of electrocardiosignal classification method of the CNN based on from coding mode in conjunction with GRU |
| CN109770861A (en) * | 2019-03-29 | 2019-05-21 | 广州视源电子科技股份有限公司 | Method, device, equipment and storage medium for training and detecting cardioelectric rhythm model |
| CN110750770A (en) * | 2019-08-18 | 2020-02-04 | 浙江好络维医疗技术有限公司 | Method for unlocking electronic equipment based on electrocardiogram |
| CN110750770B (en) * | 2019-08-18 | 2023-10-03 | 浙江好络维医疗技术有限公司 | Electrocardiogram-based method for unlocking electronic equipment |
| CN111110224A (en) * | 2020-01-17 | 2020-05-08 | 武汉中旗生物医疗电子有限公司 | Electrocardiogram classification method and device based on multi-angle feature extraction |
| CN111297350A (en) * | 2020-02-27 | 2020-06-19 | 福州大学 | Three-heart beat multi-model comprehensive decision-making electrocardiogram feature classification method integrating source end influence |
| CN111557660A (en) * | 2020-06-08 | 2020-08-21 | 东北大学 | Arrhythmia identification method under sub-population deep learning framework |
| RU2823433C1 (en) * | 2023-11-21 | 2024-07-23 | Общество С Ограниченной Ответственностью "Компания "Стрим Лабс" | Method of processing and analysing electrocardiogram (ecg) data |
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