Abstract
Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.
Highlights
Sleep is an important aspect of human life and it greatly affects our mental and physical health
The classification performance was evaluated in terms of average classification accuracy (ACA), precision (Pcn), Recall (Rcl), F1-score (F1), Cohen’s Kappa (κ) value and area under curve (AUC)
Our study presented the results obtained from healthy subjects, sleep disordered patients individually as well as all subjects combined
Summary
Sleep is an important aspect of human life and it greatly affects our mental and physical health. Sleep consists of periodic repetition of unconsciousness (physical-inactivity) called non rapid eye moments (NREM)) followed by high activity called rapid eye moments (REM). To distinguish sleep’s macrostructure, sleep is categorized into five stages: wakefulness (W), N1, N2, N3, and REM, according to the guidelines of American Academy of Sleep Medicine (AASM) [1]. The sleep stages N1, N2, and N3 form NREM part of the sleep cycle followed by REM. Many studies are available on the macrostructure of sleep and researchers have developed models for automated classification of sleep stages using machine learning techniques and PSG [2,3,4,5,6,7,8,9]. Deep learning-based methods have been employed for sleep scoring [10,11,12]
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