A method for the automatic analysis of the sleep macrostructure in continuum

Sleep staging is one of the most important tasks within the context of sleep studies. For more than 40 years the gold standard to the characterization of patient's sleep macrostructure has been based on set of rules proposed by Rechtschaffen and Kales and recently modified by the American Academy of Sleep Medicine. Nevertheless the resulting map of sleep, the so-called hypnogram, has several limitations such as its low temporal resolution and the unnatural characterization of sleep through the assignment of discrete sleep states. This study reports an automatic method for the characterization of the structure of the sleep. The main intention is to overcome limitations of epoch-based sleep staging by obtaining a more continuous evolution of the sleep of the patient. The method is based on the use of fuzzy inference in order to avoid binary decisions, provide soft transitions and enable concurrent characterization of the different states. It is proven, in addition, how the new proposed continuous representation can still be used to generate the classical epoch-based hypnogram.

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

[2]  Sun K. Yoo,et al.  Genetic fuzzy classifier for sleep stage identification , 2010, Comput. Biol. Medicine.

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

[4]  Agostinho C. Rosa,et al.  Fuzzy classification of microstructural dynamics of human sleep , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[5]  A. Chesson,et al.  The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Techinical Specifications , 2007 .

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

[7]  P. Estévez,et al.  Polysomnographic pattern recognition for automated classification of sleep-waking states in infants , 2006, Medical and Biological Engineering and Computing.

[8]  S. Himanen,et al.  Limitations of Rechtschaffen and Kales. , 2000, Sleep medicine reviews.

[9]  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.

[10]  B. Kemp,et al.  A proposal for computer‐based sleep/wake analysis , 1993, Journal of sleep research.

[11]  José Carlos Príncipe,et al.  Information Theoretic fuzzy modeling for regression , 2010, International Conference on Fuzzy Systems.

[12]  P. Anderer,et al.  Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard , 2009, Journal of sleep research.

[13]  V. Moret-Bonillo,et al.  Intelligent diagnosis of sleep apnea syndrome , 2004, IEEE Engineering in Medicine and Biology Magazine.

[14]  C. Held,et al.  Expert-system classification of sleep/waking states in infants , 1999, Medical & Biological Engineering & Computing.

[15]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

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

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

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

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

[20]  L. Tarassenko,et al.  A new approach to the analysis of the human sleep/wakefulness continuum , 1996, Journal of sleep research.

[21]  Diego Álvarez-Estévez,et al.  Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome , 2009, Expert Syst. Appl..

[22]  D. Álvarez-Estévez,et al.  A continuous evaluation of the awake sleep state using fuzzy reasoning , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Vinayak Swarnkar,et al.  Objective measure of sleepiness and sleep latency via bispectrum analysis of EEG , 2010, Medical & Biological Engineering & Computing.

[24]  J.C. Principe,et al.  Sleep staging automaton based on the theory of evidence , 1989, IEEE Transactions on Biomedical Engineering.

[25]  C.A. Holzmann,et al.  Classification of sleep stages in infants: a neuro fuzzy approach , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  Alpo Värri,et al.  Computer program for automated sleep depth estimation , 2006, Comput. Methods Programs Biomed..

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

[28]  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 .

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

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

[31]  Alfredo Álvarez,et al.  Sleep stage classification using fuzzy sets and machine learning techniques , 2004, Neurocomputing.

[32]  Kwang Suk Park,et al.  Non-constraining sleep/wake monitoring system using bed actigraphy , 2006, Medical & Biological Engineering & Computing.

[33]  Natheer Khasawneh,et al.  ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR AUTOMATIC SLEEP MULTISTAGE LEVEL SCORING EMPLOYING EEG, EOG, AND EMG EXTRACTED FEATURES , 2011, Appl. Artif. Intell..

[34]  Michael C. K. Khoo,et al.  Determining a continuous marker for sleep depth , 2007, Comput. Biol. Medicine.

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