AI-enabled Algorithm for Automatic Classification of Sleep Disorders Based on Single-lead Electrocardiogram (Preprint)

BACKGROUND Healthy sleep is an essential and important physiological process for every individual to live a healthy life. Many sleep disorders are both destroying the quality and decreasing the duration of sleep. Thus, a convenient and accurate detection or classification method is important for screening and identifying sleep disorders. OBJECTIVE In this study, we proposed an AI-enabled algorithm for automatic classification of sleep disorders based on a single-lead electrocardiogram (ECG). 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). METHODS 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 5-layers 1-D 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 (7 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. RESULTS 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. CONCLUSIONS 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.