Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals

Frontiers in Physiology

Mohamed Elgendi¹  Qunfeng Tang²  Zhenyu Zheng³  Zhencheng Chen³  Yongbo Liang³  Shimin Yin⁴ 

[1] British Columbia Children’s and Women’s Hospital, Vancouver, BC, Canada;School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada;School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China;School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China;

关键词: digital health;    digital medicine;    data science;    arrhythmia detection;    BiLSTM neural network;    CNN – convolutional neural network;   

DOI  :  10.3389/fphys.2020.569050

来源: DOAJ

【 摘 要 】

Cardiovascular diseases (CVDs) have become the number 1 threat to human health. Their numerous complications mean that many countries remain unable to prevent the rapid growth of such diseases, although significant health resources have been invested toward their prevention and management. Electrocardiogram (ECG) is the most important non-invasive physiological signal for CVD screening and diagnosis. For exploring the heartbeat event classification model using single- or multiple-lead ECG signals, we proposed a novel deep learning algorithm and conducted a systemic comparison based on the different methods and databases. This new approach aims to improve accuracy and reduce training time by combining the convolutional neural network (CNN) with the bidirectional long short-term memory (BiLSTM). To our knowledge, this approach has not been investigated to date. In this study, Database I with single-lead ECG and Database II with 12-lead ECG were used to explore a practical and viable heartbeat event classification model. An evolutionary neural system approach (Method I) and a deep learning approach (Method II) that combines CNN with BiLSTM network were compared and evaluated in processing heartbeat event classification. Overall, Method I achieved slightly better performance than Method II. However, Method I took, on average, 28.3 h to train the model, whereas Method II needed only 1 h. Method II achieved an accuracy of 80, 82.6, and 85% compared with the China Physiological Signal Challenge 2018, PhysioNet Challenge 2017, and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia datasets, respectively. These results are impressive compared with the performance of state-of-the-art algorithms used for the same purpose.

【 授权许可】

Unknown