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An In-depth Analysis of Automatic Sleep Stage Categorization


Voruchu Sai Babu, Avinash S Vaidya


Vol. 22  No. 9  pp. 816-826


Sleep stage scoring is frequently done manually by sleep analysts who examine polysomnographic (PSG) data collected in sleep labs. The inspection procedure, on the other hand, is time-consuming and complex. Because of these limits, an ASSC system is more important than ever. As previously stated, the ASSC, which is the identification of discrete phases of sleep, is widely used to diagnose and treat numerous sleep disorders. The evolution and problems of multiple existing approaches for sleep stage categorization based on Electroencephalogram (EEG) data are examined in this research. The ASSC largely depends on numerous signal processing modification techniques to extract characteristics from EEG data. Previous feature extraction techniques may be classified into four categories based on their domain: time-domain, frequency-domain, time-frequency domain, and nonlinear features. We cover the benefits and downsides of various techniques in each section. We also learnt about the fundamentals of electroencephalography (EEG), the many forms of sleep disorders, and the standard EEG databases used to evaluate investigations.


Sleep stage scoring, Electroencephalogram, time-frequency features, machine learning, sleep disorders.