Automatic slow eye movement (SEM) detection of sleep onset in patients with obstructive sleep apnea syndrome (OSAS): comparison between multiple sleep latency test (MSLT) and maintenance of wakefulness test (MWT).
ABSTRACT To determine whether automatic slow eye movement (SEM) analysis performs comparably to standard sleep onset criteria at the multiple sleep latency test (MSLT) and at the maintenance of wakefulness test (MWT) in patients with obstructive sleep apnea syndrome (OSAS).
We compared sleep latencies obtained upon standard analysis of MSLT and MWT recordings with automatically detected SEM latencies in a population of 20 severe OSAS patients that randomly underwent the two tests 1 week apart.
Eight of 20 OSAS patients had EDS as answered by the Epworth Sleepiness Scale (ESS). Mean SEM latency performed comparably to standard sleep onset in both the MSLT (6.4+/-5.5 min versus 7.4+/-5.1 min, p=0.25) and the MWT (25.2+/-14.5 min versus 24.4+/-14.0 min, p=0.45) settings. Mean SEM latency significantly correlated with the sleep latency at the MSLT (r=0.52, p<0.05) and at the MWT (r=0.74, p<0.001). Finally, the Epworth Sleepiness Scale score correlated with SEM latency at the MWT (r=-0.62, p<0.01), but not at the MSLT.
Automatic SEM detection performed comparably to standard polysomnographic assessment of sleep onset, thus providing a simplified technical requirement for the MSLT and the MWT. Further studies are warranted to evaluate SEM detection of sleep onset in other sleep disorders with excessive daytime sleepiness.
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ABSTRACT: To assess the distribution across nocturnal sleep of slow eye movements (SEMs). We evaluated SEMs distribution in the different sleep stages, and across sleep cycles in nocturnal recordings of 10 healthy women. Sleep was scored according to standard criteria, and the percentage of time occupied by the SEMs was automatically detected. SEMs were differently represented during sleep stages with the following order: wakefulness after sleep onset (WASO): 61%, NREM sleep stage 1: 54%, REM sleep: 43%, NREM sleep stage 2: 21%, NREM sleep stage 3: 7%, and NREM sleep stage 4: 3% (p<0.0001). There was no difference between phasic and tonic REM sleep. SEMs progressively decreased across the NREM sleep cycles (38%, 15%, 13% during NREM sleep stage 2 in the first three sleep cycles, p=0.006), whereas no significant difference was found for REM, NREM sleep stage 1, slow-wave sleep and WASO. Our findings confirm that SEMs are a phenomenon typical of the sleep onset period, but are also found in REM sleep. The nocturnal evolution of SEMs during NREM sleep stage 2 parallels the homeostatic process underlying slow-wave sleep. SEMs are a marker of sleepiness and, potentially, of sleep homeostasis.Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 02/2011; 122(8):1556-61. · 3.12 Impact Factor
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ABSTRACT: Slow eye movements (SEMs) are typical of drowsy wakefulness and light sleep. SEMs still lack of systematic physical characterization. We present a new algorithm, which substantially improves our previous one, for the automatic detection of SEMs from the electro-oculogram (EOG) and extraction of SEMs physical parameters. The algorithm utilizes discrete wavelet decomposition of the EOG to implement a Bayes classifier that identifies intervals of slow ocular activity; each slow activity interval is segmented into single SEMs via a template matching method. Parameters of amplitude, duration, velocity are automatically extracted from each detected SEM. The algorithm was trained and validated on sleep onsets and offsets of 20 EOG recordings visually inspected by an expert. Performances were assessed in terms of correctly identified slow activity epochs (sensitivity: 85.12%; specificity: 82.81%), correctly segmented single SEMs (89.08%), and time misalignment (0.49s) between the automatically and visually identified SEMs. The algorithm proved reliable even in whole sleep (sensitivity: 83.40%; specificity: 72.08% in identifying slow activity epochs; correctly segmented SEMs: 93.24%; time misalignment: 0.49s). The algorithm, being able to objectively characterize single SEMs, may be a valuable tool to improve knowledge of normal and pathological sleep.Medical Engineering & Physics 04/2014; · 1.78 Impact Factor