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Electrocardiogram classification method based on convolutional neural network and long-term and short-term memory network

A technology of convolutional neural network and long-term and short-term memory, applied in diagnostic recording/measurement, medical science, sensors, etc., can solve problems such as difficult automatic classification of arrhythmia, and achieve the effect of improving learning efficiency

Active Publication Date: 2019-08-30
SHANDONG COMP SCI CENTNAT SUPERCOMP CENT IN JINAN
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Problems solved by technology

The electrocardiogram of the same type of arrhythmia in different stages of the same patient is likely to have obvious changes, and the difference in the electrocardiogram of the same type of arrhythmia in different patients is greater, which makes the automatic classification of arrhythmia difficult in objective aspects. big problem

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  • Electrocardiogram classification method based on convolutional neural network and long-term and short-term memory network

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Embodiment Construction

[0030] Attached below figure 1 The present invention will be further described.

[0031] A kind of electrocardiogram classification method based on convolutional neural network and long short-term memory network, comprises the steps:

[0032] a) The computer obtains the ECG data from the MIT-BIH arrhythmia database, and according to the lead records in the ECG data, select the upper signal as the signal of lead II and the lower signal as the signal of the chest lead I as the experimental data;

[0033] b) Use the dual-scale wavelet transform method to denoise the experimental data, and locate the QRS complex in the experimental data;

[0034] c) obtaining the positions of the P wave and the T wave in the electrocardiographic signal through the position of the QRS wave group, and obtaining a heart beat data;

[0035] d) Use X to denote a sample, which represents the signal data of lead II and the signal data of chest lead I, and its expanded form is X={x 1 ,x 2 ,....,x i ,...

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Abstract

The invention relates to an electrocardiogram classification method based on convolutional neural network and long-term and short-term memory network. An arrhythmia automatic classification method isrealized by multi-lead electrocardiogram data and a convolutional neural network and long-term and short-term memory network (CNN-LSTM) combined model. On one hand, compared with single-lead electrocardiogram, the multi-lead electrocardiogram contains more information, and on the other hand, the CNN-LSTM combined model combines the advantages of CNN and the advantages of LSTM. The electrocardiogram classification method has unique advantages in the respect of studying the spatial data structure and the time sequential structure. The multi-lead electrocardiogram data is used for training the CNN-LSTM, so that the learning efficiency of the network and the electrocardiogram recognizing precision can be improved.

Description

technical field [0001] The invention relates to the technical field of electrocardiogram classification, in particular to an electrocardiogram classification method based on a convolutional neural network and a long-short-term memory network. Background technique [0002] Electrocardiogram examination has become a common test item in hospitals. Electrocardiogram is the most basic indicator for doctors to judge the heart condition of patients. The electrocardiogram signal is a non-stationary periodic biological signal caused by the electrical activity of the heart, which contains a large amount of complex heart activity information, and only professionally trained doctors can accurately interpret it. Because of the complex structure of the heart and the regularity of heart activity, there are many types of arrhythmias. The electrocardiogram of the same type of arrhythmia in different stages of the same patient is likely to have obvious changes, and the difference in the elec...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0402
CPCA61B5/7264A61B5/316A61B5/318
Inventor 王英龙成曦舒明雷朱清周书旺
Owner SHANDONG COMP SCI CENTNAT SUPERCOMP CENT IN JINAN
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