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.
*Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark; †Center for Healthy Ageing, University of Copenhagen, Copenhagen, Denmark; ‡Department of Clinical Neurophysiology, and §Danish Center for Sleep Medicine, Glostrup University Hospital, Denmark.
J
J
Kempfner
G L
GL
Sorensen
M
M
Nikolic
R
R
Frandsen
H B D
HB
Sorensen
P
P
Jennum
D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D042241:Early Diagnosis; D004594:Electrophysiology / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000150:complications / Q000175:diagnosis / Q000503:physiopathology; D020187:REM Sleep Behavior Disorder / Q000175:diagnosis* / Q000503:physiopathology; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted