CN109303560A - A kind of atrial fibrillation recognition methods of electrocardiosignal in short-term based on convolution residual error network and transfer learning - Google Patents
A kind of atrial fibrillation recognition methods of electrocardiosignal in short-term based on convolution residual error network and transfer learning Download PDFInfo
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Abstract
A kind of atrial fibrillation recognition methods of electrocardiosignal in short-term based on convolution residual error network and transfer learning, this method can realize the atrial fibrillation arrhythmia cordis identified in electrocardiosignal in short-term, the treating method comprises following steps: (1) obtaining the segment of electrocardiogram (ECG) data in short-term of tape label as training data；(2) convolutional neural networks and residual unit are constructed respectively and are combined into convolution residual error network；(3) it using four disaggregated model of convolution residual error net structure and is trained, extracts the network parameter under optimal training pattern；(4) continue Optimized model using transfer learning, save the network parameter under optimal models；(5) disaggregated model for obtaining electrocardiogram (ECG) data input step (4) to be measured obtains differentiating result.Creativeness of the invention is, convolution residual error network and transfer learning are combined, and it is difficult that the atrial fibrillation caused by solving the problems, such as by signal difference alienation identifies, improves the generalization ability and robustness of model.
Description
Technical field
The present invention relates to atrial fibrillation electrocardiosignals in short-term to know field, in particular to a kind of to be moved based on convolution residual error network and depth
Move the atrial fibrillation recognition methods of electrocardiosignal in short-term of study.
Background technique
Scientific research surface, the whole world have tens of millions of people to die of heart disease every year, and heart disease is in all diseases
Disease incidence highest, the maximum disease of harm.Auricular fibrillation, abbreviation atrial fibrillation are the most common perpetual arrhythmia heart diseases.
According to statistics, the disease incidence of atrial fibrillation is 1%-2%, and the illness rate of atrial fibrillation also gradually increases with advancing age.Heart sheet
Disease of body such as heart failure, valve disease, myocardial infarction etc. is significant related to atrial fibrillation disease.Atrial fibrillation can be such that the stroke of patient sends out
Raw rate increases by 500, and is to lead to death and invalid one of the major reasons.Therefore, the timely diagnosis of the heart diseases such as atrial fibrillation
It is of great significance to improving human health, saving human life.
In recent years, with the research and development of portable intelligent ambulatory medical device, the measurement and diagnosis of electrocardiosignal are come into
People's lives.However, the rapid growth needs of electrocardiosignal take a substantial amount of time and undergo and carry out artificial screening, this increase
People's sees a doctor cost and diagnosis efficiency.Along with the development of artificial intelligence, machine is carried out by machine learning techniques and is examined automatically
The science and technology of disconnected heart disease, which is changed, becomes possibility.
Summary of the invention
In view of the defects existing in the prior art, the present invention, which provides, a kind of is learnt based on convolution residual error network and depth migration
Atrial fibrillation electrocardiosignal recognition methods in short-term, this method solve conventional machines first with the convolution residual error network in deep learning
Learn the problem low to atrial fibrillation electrocardiosignal accuracy of identification in short-term, then solves portable mobile apparatus institute using transfer learning
The problem of the sample of signal deficiency of atrial fibrillation in short-term of acquisition.
In order to solve the above technical problem, the present invention provides the following technical solutions:
A kind of atrial fibrillation recognition methods of electrocardiosignal in short-term based on convolution residual error network and transfer learning, including following step
It is rapid:
(1) segment of electrocardiogram (ECG) data in short-term of tape label is obtained as training data；
(2) convolutional neural networks and residual unit are constructed respectively and are combined into convolution residual error network；
(3) it using four disaggregated model of convolution residual error net structure and is trained, extracts the network under optimal training pattern
Parameter；
(4) continue Optimized model using transfer learning, save the network parameter under optimal models；
(5) disaggregated model for obtaining electrocardiogram (ECG) data input step (4) to be measured obtains differentiating result.
The present invention can be achieved for one section in short-term electrocardiosignal analyze, and judge its whether have the rhythm of the heart of atrial fibrillation mistake
Chang Tezheng, and then it is these four types of to provide normal signal, atrial fibrillation signal, the other diseases signal in addition to atrial fibrillation and noise signal
One of classification results.
Further, in the step (1), training dataset, can from the ecg signal data for intentionally clapping type label
It can also be the database comprising multi-source electrocardiosignal for self-built ecg signal data library, the ecg signal data library of open source；Together
When, it needs in database to include at least normal signal, atrial fibrillation signal, the other diseases signal in addition to atrial fibrillation and noise signal
These four types of labels, the label need to be marked to each electrocardiogram (ECG) data segment in short-term, which chooses with the short of above-mentioned four classes label
When electrocardiogram (ECG) data segment as training data, and need to guarantee that the quantity of above-mentioned four classes signal segment in training data is equal.
Further, in the step (4), mutually isostructural network is reinitialized, and carry out feature migration, will extracted
To optimized parameter be assigned to new migration network, including following sub-step:
(4-1) selected part testing data constructs electrocardiosignal fragment data collection, and data set includes normal signal, atrial fibrillation letter
Number, the other diseases signal in addition to atrial fibrillation and noise signal these fourth types label；
(4-2) construction migrates networks with the mutually isostructural more classification of reel product residual error network and carries out feature migration, that is, mentions
It takes the parameter under reel product residual error network optimal models and is assigned to new migration network；
(4-3) utilizes step (4-1) data set obtained, and the part convolution residual error layer of migration network is continued to do and optimized
Training, adjusting parameter；
(4-4) repeats step (4-3), after carrying out multiple parameter optimization, saves optimal models structure.
Further, the treatment process of the step (4-3) is as follows:
(4-3-1) freezes to migrate the preceding N1 layer parameter in network, i.e., fixed preceding N1 layers of parameter is not processed；
(4-3-2) is finely adjusted the preceding N2 layer parameter in rest network, is optimized to optimized parameter；
(4-3-3) reinitializes last N3 layer network in migration network, and re -training is to optimized parameter.
Beneficial effects of the present invention are mainly manifested in: solving the hardly possible of electrocardiosignal classification in short-term using convolutional neural networks
Problem, and the more common convolutional neural networks of convolution residual error network have more advantage, arithmetic accuracy is higher；Meanwhile transfer learning and volume
Product residual error network integration can well solve by signal difference alienation caused by the low problem of atrial fibrillation discrimination, improve model
Generalization ability and robustness.
Detailed description of the invention
Fig. 1 is that the present invention is based on the atrial fibrillation recognition methods processes of electrocardiosignal in short-term of convolution residual error network and transfer learning
Figure.
Fig. 2 is the convolution residual error network diagram constructed in the embodiment of the present invention.
Fig. 3 is the three kinds of migration network structures constructed in the embodiment of the present invention.
Specific embodiment
Specific implementation process of the present invention is described further with reference to the accompanying drawings of the specification.
Referring to Fig.1~Fig. 3, a kind of atrial fibrillation of electrocardiosignal in short-term identification side based on convolution residual error network and transfer learning
Method, comprising the following steps:
(1) segment of electrocardiogram (ECG) data in short-term of tape label is obtained as training data；
(2) convolutional neural networks and residual unit are constructed respectively and are combined into convolution residual error network；
(3) it using four disaggregated model of convolution residual error net structure and is trained, extracts the network under optimal training pattern
Parameter；
(4) continue Optimized model using transfer learning, save the network parameter under optimal models；
(5) disaggregated model for obtaining electrocardiogram (ECG) data input step (4) to be measured obtains differentiating result.
Referring to Fig.1, this method can realize for one section in short-term electrocardiosignal analyzed, and judge whether it has atrial fibrillation
Arrhythmia cordis feature, and then provide normal signal, atrial fibrillation signal, the other diseases signal in addition to atrial fibrillation and noise letter
One of number these four types of classification results.
Referring to Fig.1, in the step (1), training dataset, can from the ecg signal data for intentionally clapping type label
It can also be the database comprising multi-source electrocardiosignal for self-built ecg signal data library, the ecg signal data library of open source；Together
When, it needs in database to include at least normal signal, atrial fibrillation signal, the other diseases signal in addition to atrial fibrillation and noise signal
These four types of labels, the label need to be marked to each electrocardiogram (ECG) data segment in short-term, which chooses with the short of above-mentioned four classes label
When electrocardiogram (ECG) data segment as training data, and need to guarantee that the quantity of above-mentioned four classes signal segment in training data is equal.
Referring to Fig.1, Fig. 2, in the present embodiment, the convolution residual error network of step (2) building includes 1 non-residual error mould
Block and 16 residual error modules, specific structure are as follows:
Non- 1 structure of residual error module:
1) first layer: one-dimensional convolutional layer, convolution kernel is having a size of N, number of filter M, step-length S；
2) second layer: linear R eLu activation primitive layer；
3) third layer: batch normalization layer.
1 structure of residual error module:
1) first layer: one-dimensional convolutional layer, convolution kernel is having a size of N, number of filter M, step-length S；
2) second layer: batch normalization layer；
3) third layer: linear R eLu activation primitive layer；
4) the 4th layer: random drop layer, drop probability P；
5) layer 5: one-dimensional convolutional layer, convolution kernel is having a size of N, number of filter M, step-length S；
6) layer 6: one-dimensional maximum pond layer, pond window size are 2, step-length 2；
7) residual error layer: one-dimensional maximum pond layer, pond window size are 2, and the input of step-length 2, residual error layer comes from upper one
The last layer of the last layer of module, output and this module carries out linear superposition and constitutes residual error module.
2 structure of residual error module:
1) first layer: batch normalization layer；
2) second layer: linear R eLu activation primitive layer；
3) third layer: random drop layer, drop probability P；
4) the 4th layer: one-dimensional convolutional layer, convolution kernel is having a size of N, number of filter M, step-length S；
5) layer 5: batch normalization layer；
6) layer 6: linear R eLu activation primitive layer；
7) layer 7: random drop layer, drop probability P；
8) the 8th layer: one-dimensional convolutional layer, convolution kernel is having a size of N, number of filter M, step-length S；
9) residual error layer: one-dimensional maximum pond layer, pond window size are 2, and the input of step-length 2, residual error layer comes from upper one
The last layer of the last layer of module, output and this module carries out linear superposition and constitutes residual error module.
3 structure of residual error module:
1) first layer: batch normalization layer；
2) second layer: linear R eLu activation primitive layer；
3) third layer: random drop layer, drop probability P；
4) the 4th layer: one-dimensional convolutional layer, convolution kernel is having a size of N, number of filter M, step-length S；
5) layer 5: batch normalization layer；
6) layer 6: linear R eLu activation primitive layer；
7) layer 7: random drop layer, drop probability P；
8) the 8th layer: one-dimensional convolutional layer, convolution kernel is having a size of N, number of filter M, step-length S；
9) the 9th layer: one-dimensional maximum pond layer, pond window size are 2, step-length 2；
10) residual error layer: one-dimensional maximum pond layer, pond window size are 2, and the input of step-length 2, residual error layer comes from upper one
The last layer of the last layer of module, output and this module carries out linear superposition and constitutes residual error module.
Residual error module 4 and module 2 are identical.
Residual error module 5 and module 3 are identical.
The convolutional layer structure and module 2 of residual error module 6 are identical, and the 8th layer of convolution filter number is 2M.Residual error module 6
Residual error layer is one-dimensional volume base, and convolution kernel is having a size of N, number of filter 2M, step-length S.
7 structure of residual error module and module 3 are identical, and the 4th layer and the 8th layer of convolution filter number is 2M.
8 structure of residual error module and module 2 are identical, and the 4th layer and the 8th layer of convolution filter number is 2M.
Convolution module 9 and module 7 are identical.
10 structure of convolution module is identical with 6 structure of module, and the 4th layer of convolution filter number is 2M, the 8th layer of convolutional filtering
Device number is 3M, and residual error layer convolution filter number is 3M.
11 structure of convolution module and module 3 are identical, and the 4th layer and the 8th layer of convolution filter number is 3M.
12 structure of convolution module and module 2 are identical, and the 4th layer and the 8th layer of convolution filter number is 3M.
13 structure of convolution module and module 11 are identical.
14 structure of convolution module is identical with 6 structure of module, and the 4th layer of convolution filter number is 3M, the 8th layer of convolutional filtering
Device number is 4M, and residual error layer convolution filter number is 4M.
15 structure of convolution module and module 3 are identical, and the 4th layer and the 8th layer of convolution filter number is 4M.
16 structure of convolution module and module 2 are identical, and the 4th layer and the 8th layer of convolution filter number is 4M.
Referring to Fig.1, in the step (4), mutually isostructural network is reinitialized, and carry out feature migration, will extracted
To optimized parameter be assigned to new migration network, including following sub-step:
(4-1) selected part testing data constructs electrocardiosignal fragment data collection, and data set includes normal signal, atrial fibrillation letter
Number, the other diseases signal in addition to atrial fibrillation and noise signal these fourth types label；
(4-2) construction migrates networks with the mutually isostructural more classification of reel product residual error network and carries out feature migration, that is, mentions
It takes the parameter under reel product residual error network optimal models and is assigned to new migration network；
(4-3) utilizes step (4-1) data set obtained, and the part convolution residual error layer of migration network is continued to do and optimized
Training, adjusting parameter；
(4-4) repeats step (4-3), after carrying out multiple parameter optimization, saves optimal models structure.
Further, the treatment process of the step (4-3) is as follows:
(4-3-1) freezes to migrate the preceding N1 layer parameter in network, i.e., fixed preceding N1 layers of parameter is not processed；
(4-3-2) is finely adjusted the preceding N2 layer parameter in rest network, is optimized to optimized parameter；
(4-3-3) reinitializes last N3 layer network in migration network, and re -training is to optimized parameter.
Referring to Fig.1, Fig. 2, in the present embodiment, the corresponding migration network of step (4) specifically includes following three kinds of migrations knot
Structure:
1) construction is identical with reel product residual error network structure, and the last layer is that the migration network progress feature of four classification is moved
It moves, that is, extract the parameter under reel product residual error network optimal models and is assigned to new migration network, four kinds of classifications are as follows: normal letter
Number, atrial fibrillation signal, other abnormal signals, noise signal.
2.1) construction is identical with reel product residual error network structure, and the last layer is that the migration network progress feature of three classification is moved
It moves, that is, extract the parameter under reel product residual error network optimal models and is assigned to new migration network, three kinds of classifications are respectively as follows: just
Regular signal, abnormal signal and noise signal.
2.2) it is identical with reel product residual error network structure to construct another, the last layer is the migration network of two classification, right
Abnormal electrocardiogram signal is classified, that is, is extracted the parameter under reel product residual error network optimal models and be assigned to new migration net
Network, two categories are respectively atrial fibrillation signal and other abnormal signals.
3.1) construction is identical with reel product residual error network structure, and the last layer is that the migration network progress feature of two classification is moved
It moves, that is, extract the parameter under reel product residual error network optimal models and is assigned to new migration network, two categories, which are respectively as follows:, makes an uproar
Acoustical signal and non-noise signal.
3.2) it is identical with reel product residual error network structure to construct another, the last layer is the migration network of two classification, i.e.,
It extracts the parameter under reel product residual error network optimal models and is assigned to new migration network, two categories are respectively as follows: normal letter
Number and abnormal signal.
3.3) it is identical with reel product residual error network structure to reconstruct another, the last layer is the migration network of two classification,
It extracts the parameter under reel product residual error network optimal models and is assigned to new migration network, two categories are respectively as follows: atrial fibrillation
Signal and other abnormal signals.
Technical solution of the present invention is described in detail in embodiment described above, and the present invention can be directed to short
When electrocardiosignal carry out atrial fibrillation arrhythmia cordis identification.Embodiments above is merely to illustrate the present invention, rather than to of the invention
Limitation.Although the invention is described in detail with reference to an embodiment, those skilled in the art should understand that, to this hair
Bright technical solution carries out various combinations, modification or equivalent replacement, without departure from the spirit and scope of technical solution of the present invention,
It should all cover in scope of the presently claimed invention.
Claims (4)
1. a kind of atrial fibrillation recognition methods of electrocardiosignal in short-term based on convolution residual error network and transfer learning, which is characterized in that institute
State method the following steps are included:
(1) segment of electrocardiogram (ECG) data in short-term of tape label is obtained as training data；
(2) convolutional neural networks and residual unit are constructed respectively and are combined into convolution residual error network；
(3) it using four disaggregated model of convolution residual error net structure and is trained, extracts the network parameter under optimal training pattern；
(4) continue Optimized model using transfer learning, save the network parameter under optimal models；
(5) disaggregated model for obtaining electrocardiogram (ECG) data input step (4) to be measured obtains differentiating result.
2. a kind of atrial fibrillation of electrocardiosignal in short-term identification side based on convolution residual error network and transfer learning as described in claim 1
Method, which is characterized in that in the step (1), training dataset can be from the ecg signal data for intentionally clapping type label
Self-built ecg signal data library, open source ecg signal data library, can also be the database comprising multi-source electrocardiosignal；Together
When, it needs in database to include at least normal signal, atrial fibrillation signal, the other diseases signal in addition to atrial fibrillation and noise signal
These four types of labels, the label need to be marked to each electrocardiogram (ECG) data segment in short-term, which chooses with the short of above-mentioned four classes label
When electrocardiogram (ECG) data segment as training data, and need to guarantee that the quantity of above-mentioned four classes signal segment in training data is equal.
3. as claimed in claim 1 or 2 a kind of based on the knowledge of the atrial fibrillation of electrocardiosignal in short-term of convolution residual error network and transfer learning
Other method, which is characterized in that in the step (4), reinitialize mutually isostructural network, and carry out feature migration, will mention
The optimized parameter got is assigned to new migration network, including following sub-step:
(4-1) selected part testing data construct electrocardiosignal fragment data collection, data set include normal signal, atrial fibrillation signal,
Other diseases signal and noise signal these fourth types label in addition to atrial fibrillation；
(4-2) construction migrates networks with the mutually isostructural more classification of reel product residual error network and carries out feature migration, that is, extracts former
Parameter under convolution residual error network optimal models is simultaneously assigned to new migration network；
(4-3) utilizes step (4-1) data set obtained, continues to do optimization instruction to the part convolution residual error layer of migration network
Practice, adjusting parameter；
(4-4) repeats step (4-3), after carrying out multiple parameter optimization, saves optimal models structure.
4. a kind of atrial fibrillation of electrocardiosignal in short-term identification side based on convolution residual error network and transfer learning as claimed in claim 3
Method, which is characterized in that the treatment process of the step (4-3) is as follows:
(4-3-1) freezes to migrate the preceding N1 layer parameter in network, i.e., fixed preceding N1 layers of parameter is not processed；
(4-3-2) is finely adjusted the preceding N2 layer parameter in rest network, is optimized to optimized parameter；
(4-3-3) reinitializes last N3 layer network in migration network, and re -training is to optimized parameter.
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