Automatic detection of the wake and stage 1 sleep stages using the EEG sub-epoch approach

Studies by Rechtschaffen and Kales (R&K), rely on 30-sec epochs to score sleep stages. In this paper, we introduce a new approach based on three consecutive and non-consecutive 6-sec sub-epochs for the detection of the wake stage and stage 1 sleep. The Relative Spectral Energy Band (RSEB) is used as a feature extraction from the electroencephalographic (EEG) signal. Spectral estimation is performed using non-parametric and parametric methods. We then compared the performance of the conventional 30-sec epochs with the three consecutive and non-consecutive 6-sec epochs. The outcomes of this study showed that while the accuracy varies between subjects, the non-parametric method proved to be more effective with stage 1 sleep detection and the parametric method was more effective for wake stage detection. The non-consecutive sub-epoch method was more effective and consecutive method was least effective in non-parametric stage 1 detection. Alternatively, the 30-second epoch method was most effective for parametric wake stage detection.

[1]  Richard B. Berry,et al.  Fundamentals of Sleep Medicine , 2011 .

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

[3]  A. Chesson,et al.  The American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications , 2007 .

[4]  B. Porat,et al.  Digital Spectral Analysis with Applications. , 1988 .

[5]  H. Dickhaus,et al.  Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA , 2010, Methods of Information in Medicine.

[6]  A. Rechtschaffen.  A manual of standardized terminology, techniques and scoring system for sleep of human subjects , 1968 .

[7]  C.A. Holzmann,et al.  Classification of sleep stages in infants: a neuro fuzzy approach , 2002, IEEE Engineering in Medicine and Biology Magazine.

[8]  H. Nazeran,et al.  EEG feature extraction for classification of sleep stages , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  H. Schulz,et al.  Rethinking sleep analysis. , 2008, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[10]  Georg Dorffner,et al.  A reliable probabilistic sleep stager based on a single EEG signal , 2005, Artif. Intell. Medicine.

[11]  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.

[12]  M. Pooyan,et al.  Classification of EEG signals in four groups, including healthy subjects with open/closed eyes and epilepsy subjects with/without seizure by PSD estimate (using the multitaper method) and ANN , 2010, 2010 5th International Symposium on Health Informatics and Bioinformatics.

[13]  J. Mattout,et al.  Automatic analysis of single-channel sleep EEG: validation in healthy individuals. , 2007, Sleep.

[14]  Yaacob Sazali,et al.  Classification of human emotion from EEG using discrete wavelet transform , 2010 .

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

[16]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[17]  Elena Hernández-Pereira,et al.  A method for the automatic analysis of the sleep macrostructure in continuum , 2013, Expert Syst. Appl..