Affiliations: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
Correspondence:
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Corresponding author. Yongquan Liang, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China. E-mail: lyq@sdust.edu.cn.
Abstract: The electrocardiogram (ECG) signal is a kind of time-varying signal, which has the characteristics and difficulties of variability, instability, and noise. Aiming at that, this paper put forward a novel 13-layer deep dynamic neural network model (DDNN) for the ECG signal learning and classification. The proposed DDNN model is a dynamic hybrid deep learning model. It includes a wavelet block, a convolutional block, a recurrent block, and a classification block, which combines the learning property and classification mechanism of convolutional neural network for the large-scale data sets, the learning and memory ability of Long Short-Term Memory (LSTM) for time series, and the noise reduction and processing ability of wavelet basis for the signals to meet the requirement of the learning and classification of ECG signal characteristics. Sufficient experimental results show that the proposed model is feasible and effective in the electrocardiogram signal pattern classification.
Keywords: Dynamic signal classification, deep dynamic neural network, time-varying signal, ECG signal classification
