Early Automatic Detection of Parkinson's Disease Based on Sleep Recordings

Summary: Idiopathic rapid-eye-movement (REM) sleep behavior disorder (iRBD) is most likely the earliest sign of Parkinson's Disease (PD) and is characterized by REM sleep without atonia (RSWA) and consequently increased muscle activity. However, some muscle twitching in normal subjects occurs during REM sleep. Purpose: There are no generally accepted methods for evaluation of this activity and a normal range has not been established. Consequently, there is a need for objective criteria. Method: In this study we propose a full-automatic method for detection of RSWA. REM sleep identification was based on the electroencephalography and electrooculography channels, while the abnormal high muscle activity was detected from the electromyography channels, in this case the submentalis combined with left and right anterior tibialis. RSWA was identified by considering it an outlier problem, in which the number of outliers during REM sleep was used as a quantitative measure of muscle activity. Results: The proposed method was able to automatically separate all iRBD test subjects from healthy elderly controls and subjects with periodic limb movement disorder. Conclusion: The proposed work is considered a potential automatic method for early detection of PD.

[1]  R. Stafford,et al.  Principles and Practice of Sleep Medicine , 2001 .

[2]  Thomas Philip Runarsson,et al.  Automatic Sleep Staging using Support Vector Machines with Posterior Probability Estimates , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[3]  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.

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

[5]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

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

[7]  Yves Dauvilliers,et al.  Polysomnographic diagnosis of idiopathic REM sleep behavior disorder , 2010, Movement disorders : official journal of the Movement Disorder Society.

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

[9]  Lynn Marie Trotti,et al.  Phasic muscle activity in sleep and clinical features of Parkinson disease , 2010, Annals of neurology.

[10]  Donald L Bliwise,et al.  Quantification of Electromyographic Activity During Sleep: A Phasic Electromyographic Metric , 2006, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

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

[12]  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.

[13]  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.

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

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

[16]  Donald L Bliwise,et al.  Elevated PEM (phasic electromyographic metric) rates identify rapid eye movement behavior disorder patients on nights without behavioral abnormalities. , 2008, Sleep.

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

[18]  S. Krishnan,et al.  Chin EMG analysis for REM sleep behavior disorders , 2012, 2012 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC).

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

[20]  G. Plazzi,et al.  A quantitative statistical analysis of the submentalis muscle EMG amplitude during sleep in normal controls and patients with REM sleep behavior disorder , 2008, Journal of sleep research.

[21]  George Vachtsevanos,et al.  Phasic Electromyographic Metric detection based on wavelet analysis , 2011, 2011 19th Mediterranean Conference on Control & Automation (MED).

[22]  Sheng-Fu Liang,et al.  A rule-based automatic sleep staging method , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  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.

[24]  P. Jennum,et al.  Rapid Eye Movement Sleep Behavior Disorder as an Outlier Detection Problem , 2014, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

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

[26]  U. Rajendra Acharya,et al.  Analysis and Automatic Identification of Sleep Stages Using Higher Order Spectra , 2010, Int. J. Neural Syst..

[27]  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.

[28]  Rakesh Kumar Sinha,et al.  Artificial Neural Network and Wavelet Based Automated Detection of Sleep Spindles, REM Sleep and Wake States , 2008, Journal of Medical Systems.

[29]  Jean Gotman,et al.  Detection of rapid-eye movements in sleep studies , 2005, IEEE Transactions on Biomedical Engineering.

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

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

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

[33]  E. Tolosa,et al.  Quantification of electromyographic activity during REM sleep in multiple muscles in REM sleep behavior disorder. , 2008, Sleep.

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

[35]  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.

[36]  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.

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

[38]  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.

[39]  Jacques Montplaisir,et al.  Polysomnographic features of REM sleep behavior disorder , 1992, Neurology.

[40]  S Noachtar,et al.  Die REM-Schlaf-Verhaltensstörung , 2000, Der Nervenarzt.