Automatic detection of slow wave sleep using two channel electro-oculography

An automatic method was developed for detecting slow wave sleep (SWS). The automatic method is based on a two-channel electro-oculography (EOG) with left mastoid (M1) as reference. Synchronous electroencephalographic (EEG) activity was detected by calculating cross-correlation between the two EOG channels by using 0.5-6 Hz band. An amplitude criterion was used for detecting slow waves and beta power 18-30 Hz was used to exclude artefacts. The automatic scoring was compared to a standard visual sleep scoring based on EOG, central EEG and submental EMG. Sleep EEG and EOG were recorded from 265 subjects. The optimal cross-correlation, amplitude and beta thresholds were derived using data from 133 training subjects and then applied to the data from different 132 validation subjects. Results were most sensitive to the changes in the amplitude criteria. Cohen's Kappa between the visual and the new developed automatic scoring in separating non-SWS and SWS was substantial (0.70) with epoch-by-epoch agreement of 93%. SWS epoch detection sensitivity was 75% and specificity was 96%. Also the total amount of slow waves, slow wave time (SWT), was estimated. The advantage of the automatic method is that it could be applied during online recordings using only four disposable self-adhesive electrodes.

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

[2]  Aeilko H. Zwinderman,et al.  Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG , 2000, IEEE Transactions on Biomedical Engineering.

[3]  Piotr J. Durka,et al.  High resolution parametric description of slow wave sleep , 2005, Journal of Neuroscience Methods.

[4]  I. Feinberg,et al.  The adolescent decline of NREM delta, an indicator of brain maturation, is linked to age and sex but not to pubertal stage. , 2006, American journal of physiology. Regulatory, integrative and comparative physiology.

[5]  I. Feinberg,et al.  Changes in frequency and amplitude of delta activity during sleep. , 1975, Electroencephalography and clinical neurophysiology.

[6]  A. Muzet,et al.  Sleep inertia. , 2000, Sleep medicine reviews.

[7]  A Värri,et al.  Nonlinear eye movement detection method for drowsiness studies. , 1996, International journal of bio-medical computing.

[8]  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.

[9]  Martin Buchheit,et al.  Is slow wave sleep an appropriate recording condition for heart rate variability analysis? , 2005, Autonomic Neuroscience.

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

[11]  Atte Joutsen,et al.  Visual Assessment of Selected High Amplitude Frontopolar Slow Waves of Sleep: Differences between Healthy Subjects and Apnea Patients , 2004, Clinical EEG and neuroscience.

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

[13]  Hae-Jeong Park,et al.  Automated Sleep Stage Scoring Using Hybrid Rule- and Case-Based Reasoning , 2000, Comput. Biomed. Res..

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

[15]  Mark W. Mahowald,et al.  Insights from studying human sleep disorders , 2005, Nature.

[16]  D. Dijk,et al.  Frontal predominance of a relative increase in sleep delta and theta EEG activity after sleep loss in humans. , 1999, Sleep research online : SRO.

[17]  H. Merica,et al.  Spectral Power Time-courses of Human Sleep EEG Reveal a Striking Discontinuity at ∼18 Hz Marking the Division between NREM-specific and Wake/REM-specific Fast Frequency Activity , 2005 .

[18]  Lotfi Senhadji,et al.  Blind source separation for ambulatory sleep recording , 2006, IEEE Transactions on Information Technology in Biomedicine.

[19]  Kazuhiko Fukuda,et al.  Proposed supplements and amendments to ‘A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects’, the Rechtschaffen & Kales (1968) standard , 2001, Psychiatry and clinical neurosciences.

[20]  R. D. Joseph,et al.  Pattern recognition of EEG-EOG as a technique for all-night sleep stage scoring. , 1972, Electroencephalography and clinical neurophysiology.

[21]  A. Schlögl,et al.  An E-Health Solution for Automatic Sleep Classification according to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 × 7 Utilizing the Siesta Database , 2005, Neuropsychobiology.

[22]  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.

[23]  C. Doré,et al.  EEG electrode positions outside the hairline to monitor sleep in man. , 1984, Sleep.

[24]  K. Müller,et al.  The effect of an irregular shift system on sleepiness at work in train drivers and railway traffic controllers , 2002, Journal of sleep research.

[25]  A A Borbély,et al.  Recording the sleep EEG with periorbital skin electrodes. , 1995, Electroencephalography and clinical neurophysiology.

[26]  P. Ktonas,et al.  Spectral analysis vs. period-amplitude analysis of narrowband EEG activity: a comparison based on the sleep delta-frequency band. , 1981, Sleep.

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

[28]  Alpo Värri,et al.  Anteroposterior Difference in EEG Sleep Depth Measure is Reduced in Apnea Patients , 2005, Journal of Medical Systems.