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T wave shape classification based on convolutional neural network. (Chinese. English summary) Zbl 1374.92087

Summary: T wave shape classification which is helpful for the diagnosing of many cardiovascular diseases such as myocardial ischemia, acute pericarditis and sudden cardiac death, is an important research topic in electrocardiogram remote monitoring. The method of traditional T wave shape classification is based on the accurate detection of the T wave. It is implemented after the T wave delineation and feature extraction. However, T wave detection is difficult because of the position shift, morphologic variation and multi-noise. To resolve this problem, this paper proposes to classify T wave shape based on convolutional neural networks. In the new method, firstly, a candidate data segment which contains the T wave is intercepted based on the location of the QRS wave and the medical statistical knowledge. Then, the T wave is classified directly based on the convolutional neural network. Due to the advantages of sparse connection and weight share, the convolutional neural network can extract T wave feature by data training and it is robust to the poison shift and noise. So the convolutional neural network can resolve the T wave shape classification problem efficiently. The new method is tested on the MIT-BIH QT database; the experimental results show that the new method performs well in T wave shape classification without T wave delineation and the classification accuracy is 99.1 %.

MSC:

92C55 Biomedical imaging and signal processing
92B20 Neural networks for/in biological studies, artificial life and related topics
68T05 Learning and adaptive systems in artificial intelligence
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