Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging

An automatic sleep/wake stages classifier that deals with the presence of artifacts and that provides a confidence index with each decision is proposed. The decision system is composed of two stages: the first stage checks the 20s epoch of polysomnographic signals (EEG, EOG and EMG) for the presence of artifacts and selects the artifact-free signals. The second stage classifies the epoch using one classifier selected out of four, using feature inputs extracted from the artifact-free signals only. A confidence index is associated with each decision made, depending on the classifier used and on the class assigned, so that the user's confidence in the automatic decision is increased. The two-stage system was tested on a large database of 46 night recordings. It reached 85.5% of overall accuracy with improved ability to discern NREM I stage from REM sleep. It was shown that only 7% of the database was classified with a low confidence index, and thus should be re-evaluated by a physiologist expert, which makes the system an efficient decision-support tool.

[1]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

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

[3]  Sergios Theodoridis,et al.  Pattern Recognition, Fourth Edition , 2008 .

[4]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[5]  R. Pigeau,et al.  Modafinil, d‐amphetamine and placebo during 64 hours of sustained mental work. II. Effects on two nights of recovery sleep , 1995, Journal of sleep research.

[6]  J. Baranski,et al.  Modafinil, d‐amphetamine and placebo during 64 hours of sustained mental work. I. Effects on mood, fatigue, cognitive performance and body temperature , 1995, Journal of sleep research.

[7]  Daniel J Buysse,et al.  The use of polysomnography in the evaluation of insomnia. , 1995, Sleep.

[8]  A Bolz,et al.  AUTOMATED SLEEP STAGE DETECTION WITH A CLASSICAL AND A NEURAL LEARNING ALGORITHM – METHODOLOGICAL ASPECTS , 2002, Biomedizinische Technik. Biomedical engineering.

[9]  R. Moddemeijer.  On estimation of entropy and mutual information of continuous distributions , 1989 .

[10]  T. Gasser,et al.  Transformations towards the normal distribution of broad band spectral parameters of the EEG. , 1982, Electroencephalography and clinical neurophysiology.

[11]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[12]  Pierre Baconnier,et al.  Comparison between Five Classifiers for Automatic Scoring of Human Sleep Recordings , 2002, FSKD.

[13]  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.

[14]  N. T. Smith,et al.  SPECTRAL EDGE FREQUENCY — A NEW CORRELATE OF ANESTHETIC DEPTH , 1980 .

[15]  J. Röschke,et al.  Automatic recognition of rapid eye movement (REM) sleep by artificial neural networks , 1995, Journal of sleep research.

[16]  J P Macher,et al.  Neural network model: application to automatic analysis of human sleep. , 1993, Computers and biomedical research, an international journal.

[17]  R. Vasko,et al.  Muscle artifacts in the sleep EEG: Automated detection and effect on all‐night EEG power spectra , 1996, Journal of sleep research.

[18]  B. Hjorth.  EEG analysis based on time domain properties. , 1970, Electroencephalography and clinical neurophysiology.

[19]  C. Robert,et al.  Review of neural network applications in sleep research , 1998, Journal of Neuroscience Methods.

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

[21]  Suzanne Lesecq,et al.  Feature selection for sleep/wake stages classification using data driven methods , 2007, Biomed. Signal Process. Control..

[22]  J. Röschke,et al.  Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures. , 1996, Electroencephalography and clinical neurophysiology.

[23]  Jürgen Fell,et al.  Neural net classification of REM sleep based on spectral measures as compared to nonlinear measures , 2001, Biological Cybernetics.

[24]  A. Schlögl,et al.  Artifact Processing in Computerized Analysis of Sleep EEG – A Review , 1999, Neuropsychobiology.

[25]  A. Rechtschaffen.  A manual of Standardized Terminology , 1968 .

[26]  C. Robert,et al.  Adult rat vigilance states discrimination by artificial neural networks using a single EEG channel , 1996, Physiology & Behavior.

[27]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[28]  Hans L. Cycon,et al.  Sleep Stage Classification using Wavelet Transform and Neural Network , 1999 .