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Arrhythmia identification and classification method based on sparse representation and neural network

A neural network, sparse representation technique, used in character and pattern recognition, pattern recognition in signals, instrumentation, etc.

Active Publication Date: 2018-10-12
XI AN JIAOTONG UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to provide a method for identifying and classifying arrhythmia based on sparse representation and neural network, so as to solve the problem of separately considering the low-frequency band and high-frequency band of QRS wave proposed in the background technology, overcome the imbalance of sample categories, and improve the classification accuracy. question

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  • Arrhythmia identification and classification method based on sparse representation and neural network
  • Arrhythmia identification and classification method based on sparse representation and neural network

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[0079] The present invention will be further described below in conjunction with drawings and embodiments.

[0080] Such as figure 1 As shown, the arrhythmia recognition and classification method based on sparse representation and neural network provided by the present invention comprises the following steps:

[0081] (1) In the preprocessing stage, a sparse representation framework based on dictionary learning is used for noise detection and filtering. This framework mainly includes two parts, the detection of different noises and the filtering of corresponding noises.

[0082] a) In the detection of noise, it mainly detects the common baseline drift, power frequency interference and electromyographic interference in the ECG signal. Baseline drift noise in ECG signal belongs to low frequency noise, while power frequency interference and EMG interference belong to high frequency noise. The specific steps are as follows:

[0083] 1) In this step, a moving average filter is ...

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Abstract

The invention discloses an arrhythmia identification and classification method based on sparse representation and neural network. The method comprises the following steps: preprocessing electrocardiosignals by utilizing a sparse representation frame to acquire a low frequency part of QRS (Quality Rating System) wave; subsequently, realizing feature extraction by utilizing discrete cosine transform; carrying out analysis of main components to acquire transform coefficients as feature attributes after dimension reduction; and finally, carrying out automatic classification on heat beats of the six types including normal cardiac rate (N), left bundle branch block (LBBBB), right bundle branch block (RBBBB), auricular premature beat (APB), premature ventricular contraction (PVC) and pacemaker hear beat (PB) in arrhythmia by utilizing a Bagging algorithm using BP (Back Propagation) neural network as a base learner. According to the arrhythmia identification and classification method disclosed by the invention, feature extraction is carried out from a low frequency range of the QRS wave, so that the dimensionality of the feature attributes is reduced; and the problem of unbalance classification is solved by utilizing the Bagging algorithm in ensemble learning, so that the classification accuracy is improved.

Description

technical field [0001] The invention belongs to the field of electrocardiographic signal analysis, in particular to a method for recognizing and classifying arrhythmia based on sparse representation and neural network. Background technique [0002] In recent years, with the rapid development of society and economy, profound changes have taken place in the lifestyle of the people, the prevalence of risk factors for cardiovascular disease in China is obvious, and the prevalence of cardiovascular disease continues to rise. In order to deal with this problem, the prevention and treatment of various cardiovascular diseases has become an important part of medical and health care. ECG signal is the main information to assist in the diagnosis of cardiovascular diseases, which has the characteristics of large amount of data, sensitivity to noise, and difficulty in analysis. Arrhythmia is a common symptom in cardiovascular diseases, and its accurate identification can assist in the t...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/14G06F2218/06G06F2218/10G06F18/2136G06F18/2135
Inventor 王霞康春阳
Owner XI AN JIAOTONG UNIV
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