Urtnasan Erdenebayar, Joo Eun Yeon, Lee Kyu Hee
Artificial Intelligence Bigdata Medical Center, Wonju College of Medicine, Yonsei University, Wonju 26417, Korea.
Samsung Medical Center, Department of Neurology, School of Medicine, Sungkyunkwan University, Suwon 16419, Korea.
Diagnostics (Basel). 2021 Nov 5;11(11):2054. doi: 10.3390/diagnostics11112054.
Healthy sleep is an essential physiological process for every individual to live a healthy life. Many sleep disorders both destroy the quality and decrease the duration of sleep. Thus, a convenient and accurate detection or classification method is important for screening and identifying sleep disorders. In this study, we proposed an AI-enabled algorithm for the automatic classification of sleep disorders based on a single-lead electrocardiogram (ECG). An AI-enabled algorithm-named a sleep disorder network (SDN)-was designed for automatic classification of four major sleep disorders, namely insomnia (INS), periodic leg movement (PLM), REM sleep behavior disorder (RBD), and nocturnal frontal-lobe epilepsy (NFE). The SDN was constructed using deep convolutional neural networks that can extract and analyze the complex and cyclic rhythm of sleep disorders that affect ECG patterns. The SDN consists of five layers, a 1D convolutional layer, and is optimized via dropout and batch normalization. The single-lead ECG signal was extracted from the 35 subjects with the control (CNT) and the four sleep disorder groups (seven subjects of each group) in the CAP Sleep Database. The ECG signal was pre-processed, segmented at 30 s intervals, and divided into the training, validation, and test sets consisting of 74,135, 18,534, and 23,168 segments, respectively. The constructed SDN was trained and evaluated using the CAP Sleep Database, which contains not only data on sleep disorders, but also data of the control group. The proposed SDN algorithm for the automatic classification of sleep disorders based on a single-lead ECG showed very high performances. We achieved F1 scores of 99.0%, 97.0%, 97.0%, 95.0%, and 98.0% for the CNT, INS, PLM, RBD, and NFE groups, respectively. We proposed an AI-enabled method for the automatic classification of sleep disorders based on a single-lead ECG signal. In addition, it represents the possibility of the sleep disorder classification using ECG only. The SDN can be a useful tool or an alternative screening method based on single-lead ECGs for sleep monitoring and screening.
健康睡眠是每个人健康生活所必需的生理过程。许多睡眠障碍既会破坏睡眠质量，又会缩短睡眠时间。因此，一种方便、准确的检测或分类方法对于筛查和识别睡眠障碍至关重要。在本研究中，我们提出了一种基于单导联心电图（ECG）的人工智能算法，用于睡眠障碍的自动分类。设计了一种名为睡眠障碍网络（SDN）的人工智能算法，用于对四种主要睡眠障碍进行自动分类，即失眠（INS）、周期性腿部运动（PLM）、快速眼动睡眠行为障碍（RBD）和夜间额叶癫痫（NFE）。SDN使用深度卷积神经网络构建，该网络可以提取和分析影响心电图模式的睡眠障碍的复杂循环节律。SDN由五层组成，包括一个一维卷积层，并通过随机失活和批量归一化进行优化。从CAP睡眠数据库中的35名对照（CNT）受试者和四个睡眠障碍组（每组7名受试者）中提取单导联心电图信号。对心电图信号进行预处理，以30秒的间隔进行分割，并分别分为由74135、18534和23168个片段组成的训练集、验证集和测试集。使用CAP睡眠数据库对构建的SDN进行训练和评估，该数据库不仅包含睡眠障碍数据，还包含对照组数据。所提出的基于单导联心电图的睡眠障碍自动分类SDN算法表现出非常高的性能。我们分别在CNT、INS、PLM、RBD和NFE组中取得了99.0%、97.0%、97.0%、95.0%和98.0%的F1分数。我们提出了一种基于单导联心电图信号的睡眠障碍自动分类人工智能方法。此外，它代表了仅使用心电图进行睡眠障碍分类的可能性。SDN可以成为基于单导联心电图进行睡眠监测和筛查的有用工具或替代筛查方法。
