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 |
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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 journal › Article
}
TY - JOUR
T1 - Visual and computer-based detection of slow eye movements in overnight and 24-h EOG recordings
AU - Magosso, E.
AU - Ursino, M.
AU - Zaniboni, A.
AU - Provini, F.
AU - Montagna, P.
PY - 2007/5
Y1 - 2007/5
N2 - 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.
AB - 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.
KW - Automatic EOG analysis
KW - Electro-oculogram
KW - Slow eye movements
KW - Wavelet decomposition
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UR - http://www.scopus.com/inward/citedby.url?scp=34047154057&partnerID=8YFLogxK
U2 - 10.1016/j.clinph.2007.01.014
DO - 10.1016/j.clinph.2007.01.014
M3 - Article
C2 - 17368090
AN - SCOPUS:34047154057
VL - 118
SP - 1122
EP - 1133
JO - Clinical Neurophysiology
JF - Clinical Neurophysiology
SN - 1388-2457
IS - 5
ER -