Automatic analysis of single-channel sleep EEG: validation in healthy individuals.

STUDY OBJECTIVE To assess the performance of automatic sleep scoring software (ASEEGA) based on a single EEG channel comparatively with manual scoring (2 experts) of conventional full polysomnograms. DESIGN Polysomnograms from 15 healthy individuals were scored by 2 independent experts using conventional R&K rules. The results were compared to those of ASEEGA scoring on an epoch-by-epoch basis. SETTING Sleep laboratory in the physiology department of a teaching hospital. PARTICIPANTS Fifteen healthy volunteers. MEASUREMENTS AND RESULTS The epoch-by-epoch comparison was based on classifying into 2 states (wake/sleep), 3 states (wake/REM/ NREM), 4 states (wake/REM/stages 1-2/SWS), or 5 states (wake/REM/ stage 1/stage 2/SWS). The obtained overall agreements, as quantified by the kappa coefficient, were 0.82, 0.81, 0.75, and 0.72, respectively. Furthermore, obtained agreements between ASEEGA and the expert consensual scoring were 96.0%, 92.1%, 84.9%, and 82.9%, respectively. Finally, when classifying into 5 states, the sensitivity and positive predictive value of ASEEGA regarding wakefulness were 82.5% and 89.7%, respectively. Similarly, sensitivity and positive predictive value regarding REM state were 83.0% and 89.1%. CONCLUSIONS Our results establish the face validity and convergent validity of ASEEGA for single-channel sleep analysis in healthy individuals. ASEEGA appears as a good candidate for diagnostic aid and automatic ambulant scoring.

[1]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[2]  A. Rechtschaffen,et al.  A manual of standardized terminology, technique and scoring system for sleep stages of human subjects , 1968 .

[3]  E. Wolpert.  A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .

[4]  B. Everitt,et al.  Large sample standard errors of kappa and weighted kappa. , 1969 .

[5]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[6]  J M Bland,et al.  Statistical methods for assessing agreement between two methods of clinical measurement , 1986 .

[7]  M. Kerkhofs,et al.  Automated sleep scoring: a comparative reliability study of two algorithms. , 1987, Electroencephalography and clinical neurophysiology.

[8]  H. Kuwahara,et al.  Automatic real-time analysis of human sleep stages by an interval histogram method. , 1988, Electroencephalography and clinical neurophysiology.

[9]  R Ferri,et al.  Comparison between the results of an automatic and a visual scoring of sleep EEG recordings. , 1989, Sleep.

[10]  S. Kubicki,et al.  Sleep EEG evaluation: a comparison of results obtained by visual scoring and automatic analysis with the Oxford sleep stager. , 1989, Sleep.

[11]  A. Borbély,et al.  All‐night dynamics of the human sleep EEG , 1993, Journal of sleep research.

[12]  Tom Maloney,et al.  Sigma (12–16 Hz) and beta (20–28 Hz) EEG discriminate NREM and REM sleep , 1994, Brain Research.

[13]  M. Vitiello,et al.  C STAGE, automated sleep scoring: development and comparison with human sleep scoring for healthy older men and women. , 1994, Sleep.

[14]  A Limoge,et al.  Real-time sleep-wake scoring in the rat using a single EEG channel. , 1994, Sleep.

[15]  J Hasan,et al.  Past and future of computer-assisted sleep analysis and drowsiness assessment. , 1996, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[16]  A. Muzet,et al.  Sleep stage scoring using the neural network model: comparison between visual and automatic analysis in normal subjects and patients. , 1996, Sleep.

[17]  E. Sforza,et al.  Automatic Oxford-Medilog 9200 sleep staging scoring: comparison with visual analysis. , 1996, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[18]  J. Samet,et al.  The Sleep Heart Health Study: design, rationale, and methods. , 1997, Sleep.

[19]  J. Röschke,et al.  Different phase relationships between EEG frequency bands during NREM and REM sleep. , 1997, Sleep.

[20]  A. Aguirre,et al.  Disruption of sleep recovery after 36 hours of exposure to moderately bright light. , 1997, Sleep.

[21]  S. Redline,et al.  Reliability of scoring respiratory disturbance indices and sleep staging. , 1998, Sleep.

[22]  D. White,et al.  Evaluation of a computerized polysomnographic system. , 1998, Sleep.

[23]  C Berthomier,et al.  Automatic sleep EEG analysis using filter banks. , 1999, Biomedical sciences instrumentation.

[24]  R. Ferri,et al.  The time course of high-frequency bands (15–45 Hz) in all-night spectral analysis of sleep EEG , 2000, Clinical Neurophysiology.

[25]  Odile Benoit,et al.  Slow (0.7–2 Hz) and fast (2–4 Hz) delta components are differently correlated to theta, alpha and beta frequency bands during NREM sleep , 2000, Clinical Neurophysiology.

[26]  T. Penzel,et al.  Computer based sleep recording and analysis. , 2000, Sleep medicine reviews.

[27]  D. Rapoport,et al.  Interobserver agreement among sleep scorers from different centers in a large dataset. , 2000, Sleep.

[28]  Jean Gotman,et al.  Computer-assisted sleep staging , 2001, IEEE Trans. Biomed. Eng..

[29]  G. Rettinger,et al.  The use of an ambulatory, automatic sleep recording device (QUISI version 1.0) in the evaluation of primary snoring and obstructive sleep apnoea. , 2004, Clinical otolaryngology and allied sciences.

[30]  Koby Todros,et al.  Assessment of automated scoring of polysomnographic recordings in a population with suspected sleep-disordered breathing. , 2004, Sleep.

[31]  A. Schlögl,et al.  Interrater reliability between scorers from eight European sleep laboratories in subjects with different sleep disorders , 2004, Journal of sleep research.

[32]  B. Kemp,et al.  A model-based monitor of human sleep stages , 1987, Biological Cybernetics.

[33]  J. Prado,et al.  Effect of a single dose (10 mg) of zolpidem on visual and spectral analysis of sleep in young poor sleepers , 1994, Psychopharmacology.

[34]  M. Corsi-Cabrera,et al.  Power and coherent oscillations distinguish REM sleep, stage 1 and wakefulness. , 2006, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[35]  Westgate Road,et al.  Comparison of manual sleep staging with automated neural network-based analysis in clinical practice. , 2006 .

[36]  P. Anderer,et al.  Automatic sleep classification according to Rechtschaffen and Kales , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.