1. Classification of human electrocardiograms by multi-layer convolutional neural network and hyperparameter optimization
- Author
-
Yao-Mei Chen, Jinn-Tsong Tsai, Yun-Kai Tsai, Yenming J. Chen, and Wen-Hsien Ho
- Subjects
Statistics and Probability ,business.industry ,Computer science ,General Engineering ,020206 networking & telecommunications ,Pattern recognition ,02 engineering and technology ,Convolutional neural network ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Artificial Intelligence ,Hyperparameter optimization ,0202 electrical engineering, electronic engineering, information engineering ,Artificial intelligence ,business ,Multi layer - Abstract
A multi-layer convolutional neural network (MCNN) with hyperparameter optimization (HyperMCNN) is proposed for classifying human electrocardiograms (ECGs). For performance tests of the HyperMCNN, ECG recordings for patients with cardiac arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR) were obtained from three PhysioNet databases: MIT-BIH Arrhythmia Database, BIDMC Congestive Heart Failure Database, and MIT-BIH Normal Sinus Rhythm Database, respectively. The MCNN hyperparameters in convolutional layers included number of filters, filter size, padding, and filter stride. The hyperparameters in max-pooling layers were pooling size and pooling stride. Gradient method was also a hyperparameter used to train the MCNN model. Uniform experimental design approach was used to optimize the hyperparameter combination for the MCNN. In performance tests, the resulting 16-layer CNN with an appropriate hyperparameter combination (16-layer HyperMCNN) was used to distinguish among ARR, CHF, and NSR. The experimental results showed that the average correct rate and standard deviation obtained by the 16-layer HyperMCNN were superior to those obtained by a 16-layer CNN with a hyperparameter combination given by Matlab examples. Furthermore, in terms of performance in distinguishing among ARR, CHF, and NSR, the 16-layer HyperMCNN was superior to the 25-layer AlexNet, which was the neural network that had the best image identification performance in the ImageNet Large Scale Visual Recognition Challenge in 2012.
- Published
- 2021