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• W3158869048 abstract "The marvellous phenomenon of sleep that consistently takes place for about one third part of a human being life-cycle had been a subject of mystery for many physicians over century. The research to know its lifecycle leads to the primary bifurcation of its occurring stages. The specialists of this field often use the visual inspection of neurological signals to map the scoring of sleeping stages with the help of EEG signals. Usually, EEG signals for this task are further divided into five wave bands i.e. delta, theta, alpha, beta & gamma. To filter and classify these five wave bands further ahead into sub bands Butterworth band pass filter is used. The gained sub band features will be then fed to the machine learning classifiers. This time-consuming task often possesses its limitations leading to the delay of result generation process. These limitations generated the demand for development of Dynamic classification of sleep stages (DCSS). Classification of sleeping stages leads to the knowledge for accessing that channels of neurological fluxes caused via various brain waves that could possibly help in the monitoring and diagnose of sleep disorders associated with sleep cycle. To get dynamic results modern Machine Learning Algorithms & improved statistical procedures are needed to be applied. This review paper aims at presenting the need and approach for dynamic and deliverable technique to classify and detect sleep stages with sleep dataset using modern statistical procedures over single-channel EEG signals." @default.
• W3158869048 created "2021-05-10" @default.
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• W3158869048 date "2020-04-01" @default.
• W3158869048 modified "2023-09-26" @default.
• W3158869048 title "Bio-Electronic-Intelligence based Sleeping Stages Classification" @default.
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