Comput Methods Programs Biomed - Phase space and power spectral approaches for EEG-based automatic sleep-wake classification in humans: a comparative study using short and standard epoch lengths.

Tópicos

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Resumo

Sleep disorders in humans have become a public health issue in recent years. Sleep can be analysed by studying the electroencephalogram (EEG) recorded during a night's sleep. Alternating between sleep-wake stages gives information related to the sleep quality and quantity since this alternating pattern is highly affected during sleep disorders. Spectral composition of EEG signals varies according to sleep stages, alternating phases of high energy associated to low frequency (deep sleep) with periods of low energy associated to high frequency (wake and light sleep). The analysis of sleep in humans is usually made on periods (epochs) of 30-s length according to the original Rechtschaffen and Kales sleep scoring manual. In this work, we propose a new phase space-based (mainly based on Poincar? plot) algorithm for automatic classification of sleep-wake states in humans using EEG data gathered over relatively short-time periods. The effectiveness of our approach is demonstrated through a series of experiments involving EEG data from seven healthy adult female subjects and was tested on epoch lengths ranging from 3-s to 30-s. The performance of our phase space approach was compared to a 2-dimensional state space approach using the power spectral (PS) in two selected human-specific frequency bands. These powers were calculated by dividing integrated spectral amplitudes at selected human-specific frequency bands. The comparison demonstrated that the phase space approach gives better performance in the case of short as well as standard 30-s epoch lengths.

Resumo Limpo

sleep disord human becom public health issu recent year sleep can analys studi electroencephalogram eeg record night sleep altern sleepwak stage give inform relat sleep qualiti quantiti sinc altern pattern high affect sleep disord spectral composit eeg signal vari accord sleep stage altern phase high energi associ low frequenc deep sleep period low energi associ high frequenc wake light sleep analysi sleep human usual made period epoch s length accord origin rechtschaffen kale sleep score manual work propos new phase spacebas main base poincar plot algorithm automat classif sleepwak state human use eeg data gather relat shorttim period effect approach demonstr seri experi involv eeg data seven healthi adult femal subject test epoch length rang s s perform phase space approach compar dimension state space approach use power spectral ps two select humanspecif frequenc band power calcul divid integr spectral amplitud select humanspecif frequenc band comparison demonstr phase space approach give better perform case short well standard s epoch length

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