title: A state transition-based method for quantifying EEG sleep fragmentation.
abstract: Sleep fragmentation is the predominant factor causing excessive daytime sleepiness in diseases such as sleep apnea and periodic leg movement syndrome. The reference standard for quantifying sleep fragmentation is the arousal index (ArI), which is defined as the average number of arousals per hour of sleep. Arousal scoring is tedious and subjective resulting in considerable inter- and intra-rater variability. Moreover, ArI is only weakly correlated with other indicators of sleep fragmentation such as the total sleep time (TST) and the sleep efficiency (SE). This introduces consistency problems, making the ArI difficult to interpret in practice. In this article, we address these issues by proposing a novel measure of sleep fragmentation termed the weighted-transition sleep fragmentation index (chi). This new measure is derived by capturing the different sleep states transitions and assigning weights to them. A significant correlation was found between chi and all other indices of sleep fragmentation (r = 0.72, sigma = 0.0001, r = -0.59, sigma = 0.001, r = -0.72, sigma = 0.0001, respectively, for ArI, TST and SE. These results suggest that chi is an accurate and useful tool for clinical practice.
authors.affiliation: School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, 4072, Australia.
authors.forename: Vinayak
authors.initials: V
authors.lastname: Swarnkar
authors.forename: Udantha R
authors.initials: UR
authors.lastname: Abeyratne
authors.forename: Craig
authors.initials: C
authors.lastname: Hukins
authors.forename: Brett
authors.initials: B
authors.lastname: Duce
mesh_terms: D000328:Adult; D000368:Aged; D001143:Arousal / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D020189:Nocturnal Myoclonus Syndrome / Q000175:diagnosis; D012720:Severity of Illness Index; D012815:Signal Processing, Computer-Assisted; D012891:Sleep Apnea Syndromes / Q000175:diagnosis; D020919:Sleep Disorders, Intrinsic / Q000175:diagnosis*
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