PDNet: A convolutional neural network has potential to be deployed on small intelligent devices for arrhythmia diagnosis

Yang，Fei1,2; Zhang，Xiaoqing1; Zhu，Yong3

2020

10.32604/cmes.2020.010798

CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES   影响因子和分区

1526-1492

1526-1506

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 and a depthwise convolutional layer. Furthermore, an improved loss function is utilized to improve the results of heart arrhythmias classification. To verify the proposed CNN model, extensive experiments are conducted on public MIT-BIH ECG databases. The experimental results demonstrate that the proposed PDNet achieves an accuracy of 98.2% accuracy and outperforms state-of-the-art methods about 2%.

Convolutional neural network Electrocardiograms Heart arrhythmia Loss PDblock

SCI ; EI

英语

第一 ; 通讯

Engineering ; Mathematics

Engineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications

WOS:000573973000005

TECH SCIENCE PRESS

20203909226860

Deep learning ; Economic and social effects ; Learning systems ; Diseases ; Heart ; Convolutional neural networks ; Neural network models ; Computer aided diagnosis ; Computer aided instruction ; Convolution

Biomedical Engineering:461.1 ; Biological Materials and Tissue Engineering:461.2 ; Ergonomics and Human Factors Engineering:461.4 ; Biomedical Equipment, General:462.1 ; Information Theory and Signal Processing:716.1 ; Artificial Intelligence:723.4 ; Computer Applications:723.5 ; Education:901.2 ; Social Sciences:971

COMPUTER SCIENCE

2-s2.0-85091298250

Scopus

1.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
2.Cooperative Innovtion Center of Internet Healthcare,Zhengzhou University,Zhengzhou,450052,China
3.College of Computer Science and Technology,Huaqiao University,Xiamen,361021,China

Yang，Fei,Zhang，Xiaoqing,Zhu，Yong. PDNet: A convolutional neural network has potential to be deployed on small intelligent devices for arrhythmia diagnosis[J]. CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES,2020,125(1):365-382.

Yang，Fei,Zhang，Xiaoqing,&Zhu，Yong.(2020).PDNet: A convolutional neural network has potential to be deployed on small intelligent devices for arrhythmia diagnosis.CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES,125(1),365-382.

Yang，Fei,et al."PDNet: A convolutional neural network has potential to be deployed on small intelligent devices for arrhythmia diagnosis".CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES 125.1(2020):365-382.