AUTHOR=Liang Yongbo , Yin Shimin , Tang Qunfeng , Zheng Zhenyu , Elgendi Mohamed , Chen Zhencheng 

TITLE=Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals

JOURNAL=Frontiers in Physiology

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2020.569050

DOI=10.3389/fphys.2020.569050

ISSN=1664-042X

ABSTRACT=Cardiovascular diseases (CVDs) have become the number one 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 noninvasive physiological signal for CVD screening and diagnosis. For exploring the heartbeat event classification model using single or multiple leads 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 convolution 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 hours to train the model, whereas Method II needed only one hour. Method II achieved an accuracy of 80%, 82.6%, and 85% compared with the China Physiological Signal Challenge 2018, PhysioNet Challenge 2017, and MIT-BIH Arrhythmia datasets, respectively. These results are impressive compared with the performance of state-of-the-art algorithms used for the same purpose.