title: Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson's and Parkinson's disease.
abstract: BACKGROUND
  Manual scoring of sleep relies on identifying certain characteristics in polysomnograph (PSG) signals. However, these characteristics are disrupted in patients with neurodegenerative diseases.
  
  
  METHODS
  This study evaluates sleep using a topic modeling and unsupervised learning approach to identify sleep topics directly from electroencephalography (EEG) and electrooculography (EOG). PSG data from control subjects were used to develop an EOG and an EEG topic model. The models were applied to PSG data from 23 control subjects, 25 patients with periodic leg movements (PLMs), 31 patients with idiopathic REM sleep behavior disorder (iRBD) and 36 patients with Parkinson's disease (PD). The data were divided into training and validation datasets and features reflecting EEG and EOG characteristics based on topics were computed. The most discriminative feature subset for separating iRBD/PD and PLM/controls was estimated using a Lasso-regularized regression model.
  
  
  RESULTS
  The features with highest discriminability were the number and stability of EEG topics linked to REM and N3, respectively. Validation of the model indicated a sensitivity of 91.4% and a specificity of 68.8% when classifying iRBD/PD patients.
  
  
  CONCLUSIONS
  The topics showed visual accordance with the manually scored sleep stages, and the features revealed sleep characteristics containing information indicative of neurodegeneration.
  
  
  CONCLUSIONS
  This study suggests that the amount of N3 and the ability to maintain NREM and REM sleep have potential as early PD biomarkers. Data-driven analysis of sleep may contribute to the evaluation of neurodegenerative patients.
authors.affiliation: Department of Electrical Engineering, Technical University of Denmark, Orsteds Plads, Building 349, DK-2800 Kongens Lyngby, Denmark; Danish Center for Sleep Medicine, University of Copenhagen, Department of Clinical Neurophysiology, Glostrup Hospital, Entrance 5, Nordre Ringvej 57, DK-2600 Glostrup, Denmark; H. Lundbeck A/S, Ottiliavej 9, DK-2500 Valby, Denmark. Electronic address: julie.a.e.christensen@gmail.com.
authors.forename: Julie A E
authors.initials: JA
authors.lastname: Christensen
authors.affiliation: Danish Center for Sleep Medicine, University of Copenhagen, Department of Clinical Neurophysiology, Glostrup Hospital, Entrance 5, Nordre Ringvej 57, DK-2600 Glostrup, Denmark; Department of Neurology, Bispebjerg Hospital, Bispebjerg Bakke 23, DK-2400 Copenhagen, Denmark.
authors.forename: Marielle
authors.initials: M
authors.lastname: Zoetmulder
authors.affiliation: Department of Electrical Engineering, Technical University of Denmark, Orsteds Plads, Building 349, DK-2800 Kongens Lyngby, Denmark.
authors.forename: Henriette
authors.initials: H
authors.lastname: Koch
authors.affiliation: Danish Center for Sleep Medicine, University of Copenhagen, Department of Clinical Neurophysiology, Glostrup Hospital, Entrance 5, Nordre Ringvej 57, DK-2600 Glostrup, Denmark.
authors.forename: Rune
authors.initials: R
authors.lastname: Frandsen
authors.affiliation: H. Lundbeck A/S, Ottiliavej 9, DK-2500 Valby, Denmark.
authors.forename: Lars
authors.initials: L
authors.lastname: Arvastson
authors.affiliation: H. Lundbeck A/S, Ottiliavej 9, DK-2500 Valby, Denmark.
authors.forename: Søren R
authors.initials: SR
authors.lastname: Christensen
authors.affiliation: Danish Center for Sleep Medicine, University of Copenhagen, Department of Clinical Neurophysiology, Glostrup Hospital, Entrance 5, Nordre Ringvej 57, DK-2600 Glostrup, Denmark; Center for Healthy Ageing, University of Copenhagen, Blegdamsvej 3B, DK-2200 Copenhagen N, Denmark.
authors.forename: Poul
authors.initials: P
authors.lastname: Jennum
authors.affiliation: Department of Electrical Engineering, Technical University of Denmark, Orsteds Plads, Building 349, DK-2800 Kongens Lyngby, Denmark.
authors.forename: Helge B D
authors.initials: HB
authors.lastname: Sorensen
mesh_terms: D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D004569:Electroencephalography / Q000379:methods*; D004585:Electrooculography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D020189:Nocturnal Myoclonus Syndrome / Q000175:diagnosis / Q000503:physiopathology; D010300:Parkinson Disease / Q000175:diagnosis* / Q000503:physiopathology*; D017286:Polysomnography / Q000379:methods*; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages / Q000502:physiology
keywords: Automatic classification; Electroencephalography; Electrooculography; Parkinson's disease; Polysomnography; Topic modeling