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 language | English |
---|---|
Pages (from-to) | 1122-1133 |
Number of pages | 12 |
Journal | Clinical Neurophysiology |
Volume | 118 |
Issue number | 5 |
DOIs | |
Publication status | Published - May 2007 |
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