Abstract
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.
Highlights
Sleep is an essential physiological need for everyone, and it can refresh and restore the human body
The main contribution of this study is that it shows the possibility of sleep disorders classification based on ECG signal and represents the ECG feature maps that are automatically extracted by the proposed sleep disorder network (SDN) model
The proposed artificial intelligence (AI)-enabled SDN model can be applied in digital healthcare services and used for the screening and monitoring of sleep disorders in home and hospitals
Summary
Sleep is an essential physiological need for everyone, and it can refresh and restore the human body. Both the quality and quantity of sleep are very important to live a healthy life [1,2]. Many types of sleep disorders such as sleep apnea [3], insomnia (INS) [4], periodic leg movement (PLM) [5], and REM sleep behavior disorder (RBD) [6], can destroy sleep quality. Based on the physiological signals acquired via PSG for the patient, expert or licensed sleep technicians can objectively diagnose sleep disorders. The annotation and labeling of PSG recordings (presenting numerous results) by sleep technicians are laborious tasks
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