Visual and computer-based detection of slow eye movements in overnight and 24-h EOG recordings

E. Magosso, M. Ursino, A. Zaniboni, F. Provini, P. Montagna

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Objective: The present work aimed to evaluate the performance of an automatic slow eye movement (SEM) detector in overnight and 24-h electro-oculograms (EOG) including all sleep stages (1, 2, 3, 4, REM) and wakefulness. Methods: Ten overnight and five 24-h EOG recordings acquired in healthy subjects were inspected by three experts to score SEMs. Computerized EOG analysis to detect SEMs was performed on 30-s epochs using an algorithm based on EOG wavelet transform, recently developed by our group and initially validated by considering only pre-sleep wakefulness, stages 1 and 2. Results: The validation procedure showed the algorithm could identify epochs containing SEM activity (concordance index k = 0.62, 80.7% sensitivity, 63% selectivity). In particular, the experts and the algorithm identified SEM epochs mainly in pre-sleep wakefulness, stage 1, stage 2 and REM sleep. In addition, the algorithm yielded consistent indications as to the duration and position of SEM events within the epoch. Conclusions: The study confirmed SEM activity at physiological sleep onset (pre-sleep wakefulness, stage 1 and stage 2), and also identified SEMs in REM sleep. The algorithm proved reliable even in the stages not used for its training. Significance: The study may enhance our understanding of SEM meaning and function. The algorithm is a reliable tool for automatic SEM detection, overcoming the inconsistency of manual scoring and reducing the time taken by experts.

Original languageEnglish
Pages (from-to)1122-1133
Number of pages12
JournalClinical Neurophysiology
Volume118
Issue number5
DOIs
Publication statusPublished - May 2007

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Eye Movements
Wakefulness
Sleep Stages
REM Sleep
Wavelet Analysis
Healthy Volunteers
Sleep

Keywords

  • Automatic EOG analysis
  • Electro-oculogram
  • Slow eye movements
  • Wavelet decomposition

ASJC Scopus subject areas

  • Clinical Neurology
  • Physiology (medical)
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Sensory Systems

Cite this

Visual and computer-based detection of slow eye movements in overnight and 24-h EOG recordings. / Magosso, E.; Ursino, M.; Zaniboni, A.; Provini, F.; Montagna, P.

In: Clinical Neurophysiology, Vol. 118, No. 5, 05.2007, p. 1122-1133.

Research output: Contribution to journalArticle

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