Abstract

Info

Title:: Arrhythmia detection algorithm based on dilated convolutional neural network

Author(s):: QIN Moran1; LI Zhoutong2; ZHAI Yueying3; SHI Jiguang1; JI Jiewei1; CHANG Sheng1; WANG Hao1; HE Jin1; HUANG Qijun1; 1. School of Physics and Technology, Wuhan University, Wuhan 430072, China 2. Department of Cardiology, Huangpu Brunch of Shanghai Ninth Peoples Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200011, China 3. Department of Electronic Information Engineering, Wuhan Qingchuan University, Wuhan 430204, China

Keywords:: Keywords: arrhythmia neural network electrocardiogram deep learning

Abstract:: Abstract: An electrocardiogram (ECG) signal classification algorithm based on convolutional network is proposed. The algorithm adopts an atrous spatial pooling pyramid module to extract information through atrous convolution of different sizes, and aggregates the information of each channel for enhancing the ability of feature extraction and reducing the number of parameters. The study focuses on the categories of sinus rhythm, premature atrial contraction, tachycardia and bradycardia, and a real ECG data set from a hospital which contains ECG records of 75 000 different subjects is used for experiment. The results reveal that the proposed model reaches an F1 score of 0.89 on the real ECG data set, and also achieved an F1 score of 0.87 on the CinC2017 data set, which indicates that the classification algorithm has excellent feature extraction and classification capabilities, and has application prospects in the real-time classification of ECG signals.