An automatic sleep disorder detection based on EEG cross-frequency coupling and random forest model
نویسندگان
چکیده
منابع مشابه
Automatic Sleep Stages Detection Based on EEG Signals Using Combination of Classifiers
Sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. In this paper, a combination of three kinds of classifiers are proposed which classify the EEG signal into five sleep stages including Awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3 and 4 (also called Slow Wave Sleep), and REM. Twenty-five all night recordings...
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15 صفحه اولAutomatic classification of sleep stages based on the time-frequency image of EEG signals
In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obta...
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ژورنال
عنوان ژورنال: Journal of Neural Engineering
سال: 2021
ISSN: 1741-2560,1741-2552
DOI: 10.1088/1741-2552/abf773