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Published in: Medical & Biological Engineering & Computing 10/2009

01-10-2009 | Original Article

A state transition-based method for quantifying EEG sleep fragmentation

Authors: Vinayak Swarnkar, Udantha R. Abeyratne, Craig Hukins, Brett Duce

Published in: Medical & Biological Engineering & Computing | Issue 10/2009

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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 (χ). This new measure is derived by capturing the different sleep states transitions and assigning weights to them. A significant correlation was found between χ and all other indices of sleep fragmentation (r = 0.72, σ = 0.0001, r = −0.59, σ = 0.001, r = −0.72, σ = 0.0001, respectively, for ArI, TST and SE. These results suggest that χ is an accurate and useful tool for clinical practice.

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Appendix
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Metadata
Title
A state transition-based method for quantifying EEG sleep fragmentation
Authors
Vinayak Swarnkar
Udantha R. Abeyratne
Craig Hukins
Brett Duce
Publication date
01-10-2009
Publisher
Springer-Verlag
Published in
Medical & Biological Engineering & Computing / Issue 10/2009
Print ISSN: 0140-0118
Electronic ISSN: 1741-0444
DOI
https://doi.org/10.1007/s11517-009-0524-2

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