Rapid Eye Movement Sleep Behavior Disorder as an Outlier Detection Problem

Objective: Idiopathic rapid eye movement (REM) sleep behavior disorder is a strong early marker of Parkinson’s disease and is characterized by REM sleep without atonia and/or dream enactment. Because these measures are subject to individual interpretation, there is consequently need for quantitative methods to establish objective criteria. This study proposes a semiautomatic algorithm for the early detection of Parkinson’s disease. This is achieved by distinguishing between normal REM sleep and REM sleep without atonia by considering muscle activity as an outlier detection problem. Methods: Sixteen healthy control subjects, 16 subjects with idiopathic REM sleep behavior disorder, and 16 subjects with periodic limb movement disorder were enrolled. Different combinations of five surface electromyographic channels, including the EOG, were tested. A muscle activity score was automatically computed from manual scored REM sleep. This was accomplished by the use of subject-specific features combined with an outlier detector (one-class support vector machine classifier). Results: It was possible to correctly separate idiopathic REM sleep behavior disorder subjects from healthy control subjects and periodic limb movement subjects with an average validation area under the receiver operating characteristic curve of 0.993 when combining the anterior tibialis with submentalis. Additionally, it was possible to separate all subjects correctly when the final algorithm was tested on 12 unseen subjects. Conclusions: Detection of idiopathic REM sleep behavior disorder can be regarded as an outlier problem. Additionally, the EOG channels can be used to detect REM sleep without atonia and is discriminative better than the traditional submentalis. Furthermore, based on data and methodology, arousals and periodic limb movements did only have a minor influence on the quantification of the muscle activity. Analysis of muscle activity during nonrapid eye movement sleep may improve the separation even further.

[1]  Mark W. Mahowald,et al.  Delayed emergence of a parkinsonian disorder in 38% of 29 older men initially diagnosed with idiopathic rapid eye movement sleep behavior disorder , 1996, Neurology.

[2]  Thomas Penzel,et al.  Quantification of Tonic and Phasic Muscle Activity in REM Sleep Behavior Disorder , 2008, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[3]  Bradley F. Boeve,et al.  REM Sleep Behavior Disorder and REM Sleep Without Atonia as an Early Manifestation of Degenerative Neurological Disease , 2012, Current Neurology and Neuroscience Reports.

[4]  Fredrik Gustafsson,et al.  Determining the initial states in forward-backward filtering , 1996, IEEE Trans. Signal Process..

[5]  Ji Eun Kim Diagnosis of REM sleep behavior disorder , 2009 .

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

[7]  Sridhar Krishnan,et al.  Autoregressive and Cepstral Analysis of Electromyogram in Rapid Movement Sleep , 2009 .

[8]  G. Plazzi,et al.  A preliminary quantitative analysis of REM sleep chin EMG in Parkinson's disease with or without REM sleep behavior disorder. , 2012, Sleep medicine.

[9]  Sridhar Krishnan,et al.  Analysis of the electromyogram of rapid eye movement sleep using wavelet techniques , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[11]  Joan Santamaria,et al.  Normative EMG values during REM sleep for the diagnosis of REM sleep behavior disorder. , 2012, Sleep.

[12]  B. Boeve,et al.  Association of REM sleep behavior disorder and neurodegenerative disease may reflect an underlying synucleinopathy , 2001, Movement disorders : official journal of the Movement Disorder Society.

[13]  Julie A. E. Christensen,et al.  Automatic detection of REM sleep in subjects without atonia , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  José Luis Molinuevo,et al.  Rapid-eye-movement sleep behaviour disorder as an early marker for a neurodegenerative disorder: a descriptive study , 2006, The Lancet Neurology.

[15]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[16]  Jack P Callaghan,et al.  Elimination of electrocardiogram contamination from electromyogram signals: An evaluation of currently used removal techniques. , 2006, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[17]  Zhang Jing-xing,et al.  Brief introduction to second edition of International Classification of Sleep Disorders:Diagnostic and Coding Manual , 2007 .

[18]  George Forman,et al.  Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement , 2010, SKDD.

[19]  M. Vendette,et al.  Quantifying the risk of neurodegenerative disease in idiopathic REM sleep behavior disorder , 2009, Neurology.

[20]  Helge B. D. Sørensen,et al.  Automatic REM sleep detection associated with idiopathic rem sleep Behavior Disorder , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[22]  R. Postuma,et al.  Severity of REM atonia loss in idiopathic REM sleep behavior disorder predicts Parkinson disease , 2010, Neurology.

[23]  Sid Gilman,et al.  EMG variance during polysomnography as an assessment for REM sleep behavior disorder. , 2007, Sleep.

[24]  John Shawe-Taylor,et al.  Patient classi fi cation as an outlier detection problem : An application of the One-Class Support Vector Machine , 2011 .

[25]  A Värri,et al.  A simple format for exchange of digitized polygraphic recordings. , 1992, Electroencephalography and clinical neurophysiology.

[26]  Poul Jennum,et al.  P9-1 Multi-modal REM behaviour disorder detection associated with neurodegenerative diseases , 2010, Clinical Neurophysiology.

[27]  Janaina Mourão Miranda,et al.  Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine , 2011, NeuroImage.