Liang Yongbo, Yin Shimin, Tang Qunfeng, Zheng Zhenyu, Elgendi Mohamed, Chen Zhencheng
School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China.
School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China.
Front Physiol. 2020 Oct 2;11:569050. doi: 10.3389/fphys.2020.569050. eCollection 2020.
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.
心血管疾病（CVDs）已成为对人类健康的头号威胁。其众多并发症意味着，尽管已投入大量卫生资源用于预防和管理此类疾病，但许多国家仍无法阻止这些疾病的快速增长。心电图（ECG）是用于心血管疾病筛查和诊断的最重要的非侵入性生理信号。为了探索使用单导联或多导联心电图信号的心跳事件分类模型，我们提出了一种新颖的深度学习算法，并基于不同的方法和数据库进行了系统比较。这种新方法旨在通过将卷积神经网络（CNN）与双向长短期记忆网络（BiLSTM）相结合来提高准确性并减少训练时间。据我们所知，迄今为止尚未对这种方法进行过研究。在本研究中，使用单导联心电图的数据库I和12导联心电图的数据库II来探索一种实用且可行的心跳事件分类模型。对一种进化神经系统方法（方法I）和一种将CNN与BiLSTM网络相结合的深度学习方法（方法II）在处理心跳事件分类方面进行了比较和评估。总体而言，方法I的性能略优于方法II。然而，方法I平均需要28.3小时来训练模型，而方法II仅需要1小时。与2018年中国生理信号挑战赛、2017年生理网络挑战赛以及麻省理工学院-贝斯以色列女执事医疗中心（MIT-BIH）心律失常数据集相比，方法II分别达到了80%、82.6%和85%的准确率。与用于相同目的的最先进算法的性能相比，这些结果令人印象深刻。
