Yunxiang Zhao1, Jinyong Cheng1, *, Ping Zhang1, Xueping Peng2
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1615-1628, 2020, DOI:10.32604/cmc.2020.09938
- 30 June 2020
Abstract Atrial fibrillation is the most common persistent form of arrhythmia. A method
based on wavelet transform combined with deep convolutional neural network is applied
for automatic classification of electrocardiograms. Since the ECG signal is easily
inferred, the ECG signal is decomposed into 9 kinds of subsignals with different
frequency scales by wavelet function, and then wavelet reconstruction is carried out after
segmented filtering to eliminate the influence of noise. A 24-layer convolution neural
network is used to extract the hierarchical features by convolution kernels of different
sizes, and finally the softmax classifier is used to More >
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