![]() The REM and NREM manifests certain important functioning of brain including memory consolidation, brain clearance from metabolites and cellular restoration. Sleep consists of periodic repetition of unconsciousness (physical-inactivity) called non rapid eye moments (NREM)) followed by high activity called rapid eye moments (REM). Sleep is an important aspect of human life and it greatly affects our mental and physical health. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. 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. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The extracted features have been applied to different machine learning algorithms. 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 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. The study is performed using two single-channel EEG modalities and their combination. To accomplish the task, we have utilized the openly accessible CAP sleep database. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. 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. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. ![]() Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration.
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