Automatic SLEEP staging: From young aduslts to elderly patients using multi-class support vector machine

Abstract

Aging is a process that is inevitable, and makes our body vulnerable to age-related diseases. Age is the most consistent factor affecting the sleep structure. Therefore, new automatic sleep staging methods, to be used in both of young and elderly patients, are needed. This study proposes an automatic sleep stage detector, which can separate wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep using only EEG and EOG. Most sleep events, which define the sleep stages, are reduced with age. This is addressed by focusing on the amplitude of the clinical EEG bands, and not the affected sleep events. The age-related influences are then reduced by robust subject-specific scaling. The classification of the three sleep stages are achieved by a multi-class support vector machine using the one-versus-rest scheme. It was possible to obtain a high classification accuracy of 0.91. Validation of the sleep stage detector in other sleep disorders, such as apnea and narcolepsy, should be considered in future work.

Interictal spiking in adult newly-diagnosed focal epilepsy of unknown cause: The effect of age

“…Age-related changes in nocturnal sleep architecture have been reported in the literature (Kales et al, 1966;Feinberg et al, 1967;Crowley et al, 2002;Kempfner et al, 2013). In our series, the different age groups did not show gross differences in sleep parameters.…”

Detection of REM sleep behaviour disorder by automated polysomnography analysis

“…Furthermore the EMG signal helps distinguish REM from W in HCs, but this attribute is often not helpful in the context of RBD, where there can be an absence of atonia in REM. Critical to RBD diagnosis is the identification of REM sleep, and while other studies in automated sleep staging produce better results in REM sleep detection, they benefit from primarily focusing on young HCs or a relatively smaller sample size (Virkkala et al 2008;Güneş et al 2010;Fraiwan et al 2012;Kempfner et al 2012Kempfner et al , 2013bLiang et al 2012;Bajaj and Pachori 2013;Khalighi et al 2013;Imtiaz and Rodriguez-Villegas 2014;McCarty et al 2014;Sousa et al 2015;Lajnef et al 2015;Yetton et al 2016). Despite the lack of sensitivity, REM specificity remains high, which means that as long as REM is identified with a certain precision, the quantified absence of atonia will remain indicative.…”

A comparison of different machine learning algorithms using single channel EEG signal for classifying human sleep stages

“…Moreover, Multi-class Support Vector Machine based on EEG and EOG signals was used for an automatic sleep stage detector, which can separate sleep stages in young healthy subjects and elderly patients automatically. The experimental results showed that the proposed algorithm could achive 91% success rate [18].…”