Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson's and Parkinson's disease

Journal of Neuroscience Methods

Volumn 235, Issue , 2014, Pages 262-276

  Christensen, Julie A E ^a,b,c   Zoetmulder, Marielle ^b,d   Koch, Henriette ^a   Frandsen, Rune ^b   Arvastson, Lars ^c   Christensen, Søren R ^c   Jennum, Poul ^b   Sorensen, Helge B D ^a  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

^c H LUNDBECK A S   (Denmark)

^d BISPEBJERG HOSPITAL   (Denmark)

Author keywords

Automatic classification; Electroencephalography; Electrooculography; Parkinson's disease; Polysomnography; Topic modeling

Indexed keywords

ADULT; AGED; ARTICLE; CONTROLLED STUDY; DELTA RHYTHM; ELECTROENCEPHALOGRAM; ELECTROENCEPHALOGRAPHY; ELECTROMYOGRAM; ELECTROOCULOGRAPHY; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; NERVE DEGENERATION; PARASOMNIA; PARKINSON DISEASE; PERIODIC LIMB MOVEMENT DISORDER; POLYSOMNOGRAPHY; PRIORITY JOURNAL; REM SLEEP; SENSITIVITY AND SPECIFICITY; SLEEP; SLEEP SPINDLE; SLEEP STAGE; THETA RHYTHM; ALGORITHM; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; MIDDLE AGED; NOCTURNAL MYOCLONUS SYNDROME; PATHOPHYSIOLOGY; PHYSIOLOGY; PROCEDURES; REGRESSION ANALYSIS; SIGNAL PROCESSING; VALIDATION STUDY;

AGED; ALGORITHMS; ARTIFICIAL INTELLIGENCE; ELECTROENCEPHALOGRAPHY; ELECTROOCULOGRAPHY; FEMALE; HUMANS; MALE; MIDDLE AGED; MODELS, NEUROLOGICAL; NOCTURNAL MYOCLONUS SYNDROME; PARKINSON DISEASE; POLYSOMNOGRAPHY; REGRESSION ANALYSIS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; SLEEP STAGES;

EID: 84904893171     PISSN: 01650270     EISSN: 1872678X     Source Type: Journal    
DOI: 10.1016/j.jneumeth.2014.07.014     Document Type: Article

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