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Automatic sleep stage classification using two-channel electro-oculography.
- Source :
-
Journal of neuroscience methods [J Neurosci Methods] 2007 Oct 15; Vol. 166 (1), pp. 109-15. Date of Electronic Publication: 2007 Jun 28. - Publication Year :
- 2007
-
Abstract
- An automatic method for the classification of wakefulness and sleep stages SREM, S1, S2 and SWS was developed based on our two previous studies. The method is based on a two-channel electro-oculography (EOG) referenced to the left mastoid (M1). Synchronous electroencephalographic (EEG) activity in S2 and SWS was detected by calculating cross-correlation and peak-to-peak amplitude difference in the 0.5-6 Hz band between the two EOG channels. An automatic slow eye-movement (SEM) estimation was used to indicate wakefulness, SREM and S1. Beta power 18-30 Hz and alpha power 8-12 Hz was also used for wakefulness detection. Synchronous 1.5-6 Hz EEG activity and absence of large eye movements was used for S1 separation from SREM. Simple smoothing rules were also applied. Sleep EEG, EOG and EMG were recorded from 265 subjects. The system was tuned using data from 132 training subjects and then applied to data from 131 validation subjects that were different to the training subjects. Cohen's Kappa between the visual and the developed new automatic scoring in separating 30s wakefulness, SREM, S1, S2 and SWS epochs was substantial 0.62 with epoch by epoch agreement of 72%. With automatic subject specific alpha thresholds for offline applications results improved to 0.63 and 73%. The automatic method can be further developed and applied for ambulatory sleep recordings by using only four disposable, self-adhesive and self-applicable electrodes.
- Subjects :
- Algorithms
Cohort Studies
Cross-Sectional Studies
Data Interpretation, Statistical
Electroencephalography instrumentation
Electronic Data Processing instrumentation
Electrooculography instrumentation
Evoked Potentials physiology
Eye Movements physiology
Humans
Oculomotor Muscles physiology
Pattern Recognition, Automated methods
Polysomnography instrumentation
Polysomnography methods
Sleep, REM
Software standards
Brain physiology
Electroencephalography methods
Electronic Data Processing methods
Electrooculography methods
Signal Processing, Computer-Assisted instrumentation
Sleep Stages physiology
Wakefulness physiology
Subjects
Details
- Language :
- English
- ISSN :
- 0165-0270
- Volume :
- 166
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Journal of neuroscience methods
- Publication Type :
- Academic Journal
- Accession number :
- 17681382
- Full Text :
- https://doi.org/10.1016/j.jneumeth.2007.06.016