基于CNN和频率切片小波变换的T波形态分类

doi:10.3969/j.issn.0258-8021.2021.01.01

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Abstract Real-time monitoring of ECG is one important means of cardiovascular disease prevention. T-wave is an important characteristic of diseases such as myocardial ischemia and sudden cardiac death. The automatic identification of T-wave is a challenging taskin ECG remote monitoring. Due to the influence of high noise background of real time monitoring ECG, the conventional T-wave feature extraction and classification algorithm encounters a bottleneck. In this paper, a T-wave morphological recognition algorithm combining slice frequency wavelet transform and convolutional neural network was proposed. The algorithm included: locating automatically the R-wave peak's position and the T-wave ends' position to identify a segment containing the T-wave; the frequency slice wavelet transform was performed, and the generated time-frequency image was input into the convolutional neural network to complete the classification of the T-wave. The frequency slice wavelet transform transformed the signal to the time-frequency domain, which accurately presented the time-frequency energy distribution characteristics of the ECG signal. The hidden layers of the convolutional neural network completed the three features' extraction of the time-frequency image by convolving, activating and pooling the time-frequency image three times. These features have translation and scaling invariance. In this paper, 12 830 fragments in European ST-T database was used. The convolutional neural network model was trained and tested by the 3-fold cross validation method. The classification accuracy of experiment based on heart beats reached 97.34%, and the F1 measure reached 96.97%. The classification accuracy of experiment based on samples was 84.80%, and the F1 measure was 83.29%. The classification accuracy of the model tested in QT database was 87.83%, F1 measure was 85.38%, and the generalization performance was good. Compared with other T-wave classification algorithms (such as decision tree, support vector machine, etc.), the classification accuracy based on heart beat experiments was improved by 1～5%. The results demonstrated that the algorithm designed for the classification of six types of T-wave improved the accuracy and performed well in terms of robustness and generalization performance. In addition, the algorithm model was also applicable to the analysis of other physiological signals and has certain guiding significance in the field of medical image analysis.

Key words： ECG T-wave morphological classification convolutional neural network modified frequency slice wavelet transform

Received: 06 March 2020

PACS:

R318

Corresponding Authors: *E-mail: sswei@sdu.edu.cn

About author:: #（Member, Chinese Society of Biomedical Engineering）

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	Xie Jiajing
	Wei Shoushui
	Jiang Xinge
	Wang Chunyuan
	Cui Huaijie
	Liu Chengyu

Cite this article:

Xie Jiajing,Wei Shoushui,Jiang Xinge, et al. T-Wave Morphological Classification Based on CNN and Modified Frequency Slice Wavelet Transform[J]. Chinese Journal of Biomedical Engineering, 2021, 40(1): 1-11.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2021.01.01 OR http://cjbme.csbme.org/EN/Y2021/V40/I1/1

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