EEG-based Automatic Sleep-wake Classification in Humans Using Short and Standard Epoch Lengths

The alternating among sleep-wake stages gives information related to the sleep quality and quantity since this alternating pattern is highly affected during sleep disorders. 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 paper, we propose a new phase space-based algorithm (mainly based on Poincare plot) 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 spectral power 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.