School of Artificial Intelligence, Peking University, Beijing, 100871, People's Republic of China.
Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, 100871, People's Republic of China.
Physiol Meas. 2022 Aug 12;43(8). doi: 10.1088/1361-6579/ac826e.
Sleep is one of the most important human physiological activities, and plays an essential role in human health. Polysomnography (PSG) is the gold standard for measuring sleep quality and disorders, but it is time-consuming, labor-intensive, and prone to errors. Current research has confirmed the correlations between sleep and the respiratory/circulatory system. Electrocardiography (ECG) is convenient to perform, and ECG data are rich in breathing information. Therefore, sleep research based on ECG data has become popular. Currently, deep learning (DL) methods have achieved promising results on predictive health care tasks using ECG signals. Therefore, in this review, we systematically identify recent research studies and analyze them from the perspectives of data, model, and task. We discuss the shortcomings, summarize the findings, and highlight the potential opportunities. For sleep-related tasks, many ECG-based DL methods produce more accurate results than traditional approaches by combining multiple signal features and model structures. Methods that are more interpretable, scalable, and transferable will become ubiquitous in the daily practice of medicine and ambient-assisted-living applications. This paper is the first systematic review of ECG-based DL methods for sleep tasks.
睡眠是人类最重要的生理活动之一，对人类健康起着至关重要的作用。多导睡眠图（PSG）是衡量睡眠质量和障碍的金标准，但它耗时、费力且容易出错。目前的研究已经证实了睡眠与呼吸/循环系统之间的相关性。心电图（ECG）易于进行，并且 ECG 数据富含呼吸信息。因此，基于 ECG 数据的睡眠研究变得很流行。目前，深度学习（DL）方法已经在使用 ECG 信号的预测性医疗保健任务中取得了有前景的成果。因此，在本综述中，我们系统地识别了最近的研究，并从数据、模型和任务的角度对它们进行了分析。我们讨论了缺点，总结了发现，并强调了潜在的机会。对于与睡眠相关的任务，许多基于 ECG 的 DL 方法通过结合多个信号特征和模型结构，比传统方法产生更准确的结果。更具可解释性、可扩展性和可转移性的方法将在医学和环境辅助生活应用的日常实践中变得无处不在。本文是对基于 ECG 的 DL 方法在睡眠任务中的首次系统综述。
