Prediction of microsleeps using EEG inter-channel relationships [article]

Abdul Baseer Buriro, University Of Canterbury
2019
A microsleep is a brief and involuntary sleep-related loss of consciousness of up to 15 s during an active and attention-demanding task. Such episodes of unresponsiveness are of particularly high importance in people who perform high-risk and monotonous activities requiring extended- attention and unimpaired visuomotor performance, such as car and truck drivers, train drivers, pilots, and air-traffic controllers, where microsleeps can, and do, result in catastrophic accidents and fatalities.
more » ... rosleep-related accidents can potentially be avoided and thereby lives saved, if microsleeps are noninvasively and accurately predicted. The aim of this study was to explore various inter-channel relationships in the electroencephalogram (EEG) for detection/prediction of microsleeps. In addition to feature-level and decision-level data fusion techniques, ensemble classification techniques were investigated to improve microsleep detection/prediction accuracies. The data used in this research were from a previous study in which 15 healthy non-sleep- deprived participants performed two 1-h sessions of 1-D continuous tracking task. Eight subjects were included for the analyses, all of whom experienced at least one microsleep during the two sessions of the task. A gold-standard was formed by integrating two independent measures of the video-rating from a human expert and tracking performance. An average duration of microsleep was 2.16 min/h. Three univariate feature sets, namely variance, wavelet spectral power, and entropy features were extracted from individual channels of EEG. Seven bivariate and symmetric feature sets were directly extracted from pairs of EEG channels. Three of the 7 feature sets were non-normalized (i.e., covariance, wavelet cross-spectral power, joint entropy) and 4 were normalized (i.e., Pearson's correlation coefficient, wavelet coherence, mutual information, phase synchronization index (PSI)). Eleven feature sets were extracted from coefficients of a multivariate autoregressive (MVAR) model, fitted to [...]
doi:10.26021/1844 fatcat:5xswevuve5akdfs7uxssqxob54