Hong Shenda, Zhou Yuxi, Shang Junyuan, Xiao Cao, Sun Jimeng
Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
School of Electronics Engineering and Computer Science, Peking University, Beijing, China; Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China.
Comput Biol Med. 2020 Jul;122:103801. doi: 10.1016/j.compbiomed.2020.103801. Epub 2020 Jun 7.
The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals.
This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between January 1st of 2010 and February 29th of 2020 from Google Scholar, PubMed, and the Digital Bibliography & Library Project. We then analyzed each article according to three factors: tasks, models, and data. Finally, we discuss open challenges and unsolved problems in this area.
The total number of papers extracted was 191. Among these papers, 108 were published after 2019. Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and denoising.
The number of works on deep learning for ECG data has grown explosively in recent years. Such works have achieved accuracy comparable to that of traditional feature-based approaches and ensembles of multiple approaches can achieve even better results. Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. However, there are some new challenges and problems related to interpretability, scalability, and efficiency that must be addressed. Furthermore, it is also worth investigating new applications from the perspectives of datasets and methods.
This paper summarizes existing deep learning research using ECG data from multiple perspectives and highlights existing challenges and problems to identify potential future research directions.
心电图（ECG）是医学和医疗保健中最常用的诊断工具之一。深度学习方法在使用心电图信号的预测性医疗任务中取得了可观的成果。
本文从建模和应用的角度对用于心电图数据的深度学习方法进行系统综述。
我们从谷歌学术、PubMed和数字文献与图书馆项目中提取了2010年1月1日至2020年2月29日期间发表的将深度学习（深度神经网络）模型应用于心电图数据的论文。然后我们根据任务、模型和数据这三个因素对每篇文章进行分析。最后，我们讨论该领域的开放挑战和未解决的问题。
提取的论文总数为191篇。其中，108篇是2019年之后发表的。不同的深度学习架构已被用于各种心电图分析任务，如疾病检测/分类、注释/定位、睡眠分期、生物特征识别和去噪。
近年来，针对心电图数据的深度学习研究数量呈爆炸式增长。这些研究取得的准确率与传统的基于特征的方法相当，多种方法的集成甚至可以取得更好的结果。具体而言，我们发现使用专家特征的卷积神经网络和循环神经网络集成的混合架构产生了最佳结果。然而，存在一些与可解释性、可扩展性和效率相关的新挑战和问题必须加以解决。此外，从数据集和方法的角度研究新应用也值得一试。
本文从多个角度总结了现有的使用心电图数据的深度学习研究，并突出了现有挑战和问题，以确定未来潜在的研究方向。
