@Article{cmc.2020.09938,
AUTHOR = {Yunxiang Zhao, Jinyong Cheng, Ping Zhang, Xueping Peng},
TITLE = {ECG Classification Using Deep CNN Improved by Wavelet  Transform},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1615--1628},
URL = {http://www.techscience.com/cmc/v64n3/39448},
ISSN = {1546-2226},
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 classify them. This paper applies this 
method of the ECG data set provided by the 2017 PhysioNet/CINC challenge. After cross 
validation, this method can obtain 87.1% accuracy and the F1 score is 86.46%. Compared 
with the existing classification method, our proposed algorithm has higher accuracy and 
generalization ability for ECG signal data classification.},
DOI = {10.32604/cmc.2020.09938}
}