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• W4214608544 abstract "AbstractSleep is an important aspect of human body which alternatively decides the physical and mental conditions. It has been seen that several sleep-related disorders are affected with irrespective of different age group subjects. The most important part of diagnosis any types of sleep disorders is the classification of sleep stages. But the manual inspection of sleep-related behavior is quite difficult and more time-consuming process. Sometimes it leads toward several other neurological disorders. Therefore in this research work, we propose an automated sleep staging approach for the classification of the sleep stages. Mainly the proposed experiments followed three basic steps such as preprocessing feature extraction, feature selection, and classification. The entire experiment is conducted upon the ISRUC-Sleep dataset. The proposed model reported an overall accuracy of 97.73% using machine learning classification model.KeywordsElectroencephalographyAutomatic sleep stage analysisTime–frequency featuresMachine learning" @default.
• W4214608544 created "2022-03-02" @default.
• W4214608544 creator A5062662531 @default.
• W4214608544 creator A5081069413 @default.
• W4214608544 date "2022-01-01" @default.
• W4214608544 modified "2023-10-17" @default.
• W4214608544 title "Machine Learning Model for Automated Sleep Scoring Based on Single-Channel EEG Signal Data" @default.
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• W4214608544 doi "https://doi.org/10.1007/978-981-16-7182-1_30" @default.
• W4214608544 hasPublicationYear "2022" @default.
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