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ECG signal analysis method aiming at abnormal heart rhythm classification

A signal analysis method and rhythm technology, applied in medical science, instruments, biological neural network models, etc., can solve the problems of low accuracy of computer-aided processing methods, avoid inter-individual differences and intra-individual differences, stabilize classification results, and use convenient effect

Inactive Publication Date: 2019-06-21
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0004] In view of the above problems, the present invention provides an ECG signal analysis method for abnormal cardiac rhythm classification, to solve the problem of low accuracy of computer-aided processing methods based on feature engineering

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  • ECG signal analysis method aiming at abnormal heart rhythm classification
  • ECG signal analysis method aiming at abnormal heart rhythm classification
  • ECG signal analysis method aiming at abnormal heart rhythm classification

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Embodiment Construction

[0021] Further describe the technical scheme of the present invention below in conjunction with accompanying drawing:

[0022] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution in the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention and the accompanying drawings.

[0023] An ECG signal analysis method for abnormal cardiac rhythm classification, using TAG-SB-LSTM and FAM-LAM-TD-CNN to mine long-term dependencies and local features from ECG signals, and according to the location and surroundings of sampling points Fine-tune the long-term dependence of the waveform, and fine-tune the feature value according to the type and location of the extracted features, so as to obtain the accurate overall and local fluctuation modes of the ECG, and finally use the fully connected network to determine the ECG signal segment. process result....

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Abstract

The invention provides an ECG signal analysis method aiming at abnormal heart rhythm classification. A long-term dependency relationship and local features of signals can be mined from ECG signals bySB-LSTM and TD-CNN; fine adjustment of the long-term dependency relationship and local features can be realized through designed TAG, FAM and LAM, and accordingly an accurate ECG integral fluctuationmode and a local fluctuation mode can be obtained; finally, an ECG signal processing result is obtained by FCN. The method has advantages that by complete combination of integral fluctuation mode andlocal fluctuation mode information of the ECG signals, great usability in a large-scale data set is achieved; in addition, inter-individual differences and intra-individual differences resulted from manual classification are avoided, and a stable classification result can be obtained; especially, the method is free of assistance of any expert knowledge, avoids manual design of various features, feature selection treatment and individual classifier construction and is a typical end-to-end method, and the method has advantages of convenience and quickness in use, high classification precision and the like.

Description

technical field [0001] The invention relates to the field of biological health computing, in particular to an ECG signal analysis method for abnormal cardiac rhythm classification. Background technique [0002] Cardiac rhythms are usually divided into five categories: non-ectopic (N: Non-ectopic), ventricular ectopic (V: Ventricular ectopic), supraventricular ectopic (S: Supraventricular ectopic), mixed (F: Fusion ), unknown type (Q: Unknown). Each subtype often has different clinical presentations and requires different treatment approaches, so accurate classification of abnormal cardiac rhythms is a prerequisite for effective treatment. [0003] The existing abnormal cardiac rhythm classification methods are mainly divided into two categories: 1) manual inspection method based on professional doctors; 2) computer-aided classification method based on feature engineering. The former requires doctors to have extremely rich clinical experience, and it takes a lot of time and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/00A61B5/04A61B5/0402G06K9/62G06N3/04
Inventor 周兴社刘帆王柱倪红波於志文
Owner NORTHWESTERN POLYTECHNICAL UNIV
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