Abstract
The changes in social rhythm and increase in the pressure in professional sectors cause improper proper sleep on daily basis, which ultimately creates various sleep-related disorders. The first most important step for diagnosis of any type of sleep-related disorder is sleep staging. The main framework of the sleep staging system is carried through four main steps: signal preprocessing, feature extraction, feature screening, and classification. The main objective of this research work is to improve sleep staging by using multiple physiological signals such as electroencephalogram, electromyogram, and electrooculogram in an automated method. This proposed research work carried two different individual experiments conducted with the input of single-channel EEG and with combinations of all three channels to improve the accuracy of sleep staging. Besides, this research work extracted 29 features, both linear and non-linear features, which provides important information with changes in sleep behavior of the subjects and used a ReliefF weight feature selection algorithm to select the relevant features which are highly correlated with sleep stages which is helpful to the analysis of the sleep behavior characteristics. Next, the selected features were forwarded into machine learning classification models such as Random forest for sleep staging based on five sleep-disordered subjects. The subject’s data were extracted from the ISRUC-Sleep dataset. The proposed model achieved the highest classification accuracies of 99.45% and 98.44% for single-channel EEG and EEG+EOG+EMG, respectively, for five sleep classes using a random forest classifier. The proposed machine learning model is ready for the diagnosis of the different types of sleep-related disorders and can be managed with huge polysomnography records.
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Satapathy, S.K., Loganathan, D., Sharathkumar, S., Narayanan, P. (2022). A Machine Learning Model for Automated Classification of Sleep Stages using Polysomnography Signals. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1387. Springer, Singapore. https://doi.org/10.1007/978-981-16-2594-7_24
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