Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels

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.

An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting

“…Again, it can be seen from Table 9 and 10 that the values of class-specific specificity of the proposed scheme are also high for all the stages of sleep. [13]. Second, the preparation procedure for the subject becomes complicated for such methods [28].…”

“…Table 3 gives the J1 and J2 values of our method and compares with those of the two approaches in [13]. It is evident from Fig.…”

“…Conventional PSD Based Methods [13] where S m and S w denote the between-class and within-class scatter matrix respectively and tr(S) refers to the trace of square matrix S. The larger the values of J1 and J2 are, the more separable the features are in feature space. Table 3 gives the J1 and J2 values of our method and compares with those of the two approaches in [13].…”

“…Huang et al [13] employed short-time Fourier transform on two channel of forehead EEG signals and performed classification using relevance vector machine. Lajnef et al [14] employed various features such as linear prediction error energy, variance, skewness, kurtosis, permutation entropy and multi-class support vector machine on EMG, EOG, and EEG signals for computer-assisted sleep scoring.…”

A Convolutional Network for the Classification of Sleep Stages

“…Huang et al [14], study power spectral density of 2 EEG channels classifying frequency features with a modified SVM; Finally, Günes et al [15], also analyse power spectral density while classifying with a nearest neighbours algorithm.…”