Abstract
Sleep quality is important, especially given the considerable number of sleep-related pathologies. The distribution of sleep stages is a highly effective and objective way of quantifying sleep quality. As a standard multi-channel recording used in the study of sleep, polysomnography (PSG) is a widely used diagnostic scheme in sleep medicine. However, the standard process of sleep clinical test, including PSG recording and manual scoring, is complex, uncomfortable, and time-consuming. This process is difficult to implement when taking the whole PSG measurements at home for general healthcare purposes. This work presents a novel sleep stage classification system, based on features from the two forehead EEG channels FP1 and FP2. By recording EEG from forehead, where there is no hair, the proposed system can monitor physiological changes during sleep in a more practical way than previous systems. Through a headband or self-adhesive technology, the necessary sensors can be applied easily by users at home. Analysis results demonstrate that classification performance of the proposed system overcomes the individual differences between different participants in terms of automatically classifying sleep stages. Additionally, the proposed sleep stage classification system can identify kernel sleep features extracted from forehead EEG, which are closely related with sleep clinician's expert knowledge. Moreover, forehead EEG features are classified into five sleep stages by using the relevance vector machine. In a leave-one-subject-out cross validation analysis, we found our system to correctly classify five sleep stages at an average accuracy of 76.7 ± 4.0 (SD) % [average kappa 0.68 ± 0.06 (SD)]. Importantly, the proposed sleep stage classification system using forehead EEG features is a viable alternative for measuring EEG signals at home easily and conveniently to evaluate sleep quality reliably, ultimately improving public healthcare.
Highlights
Monitoring human physiology during sleep is essential for individual health
The scatter plot of the proposed feature extraction approach presents the scatter points from different groups, i.e., wakefulness, NREM stage2 (N2), NREM stage3 (N3), and rapid eye movement (REM), which are leading ones in their own industries
Most data points from the conventional Power spectrum densities (PSD) feature extraction approach are mixing in principal components (PCs) space
Summary
Monitoring human physiology during sleep is essential for individual health. Sleep is increasingly viewed as playing an important role in restitution (Akerstedt et al, 2007). As an important aspect of well-being, sleep quality is closely related to overall quality of life, life satisfaction, secretion of the stress hormone, cortisol, and inadequate immunity (Gallagher et al, 2010). Sleep stages are recorded for clinical diagnosis and the treatment of sleep disorders. Sleep quality is most closely related to the distribution of depth of sleep; sufficient sleep quality must reach adequate deep sleep. The depth of sleep is characterized by different cortical electrical activities. Several sleep stages can be defined by variations of cortical electrical activities and other physiological signals, i.e., muscle activity and eye movement. N3 reflects slow wave sleep (SWS, R&K stages S3 + S4)
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