Fei Yang1,2,#, Xiaoqing Zhang1,*,#, Yong Zhu3
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 365-382, 2020, DOI:10.32604/cmes.2020.010798
Abstract Heart arrhythmia is a group of irregular heartbeat conditions and
is usually detected by electrocardiograms (ECG) signals. Over the past years,
deep learning methods have been developed to classify different types of
heart arrhythmias through ECG based on computer-aided diagnosis systems (CADs), but these deep learning methods usually cannot trade-off
between classification performance and parameters of deep learning methods.
To tackle this problem, this work proposes a convolutional neural network
(CNN) model named PDNet to recognize different types of heart arrhythmias
efficiently. In the PDNet, a convolutional block named PDblock is devised,
which is comprised of a pointwise convolutional layer… More >
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