1 Department of Electrical Engineering, Technical University of Denmark2 Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark3 Department of Applied Mathematics and Computer Science, Technical University of Denmark4 Statistics and Data Analysis, Department of Applied Mathematics and Computer Science, Technical University of Denmark5 University of Copenhagen6 Department of Informatics and Mathematical Modeling, Technical University of Denmark7 H. Lundbeck A/S8 Copenhagen Center for Health Technology, Center, Technical University of Denmark
Background: Manual scoring of sleep relies on identifying certain characteristics in polysomnograph (PSG) signals. However, these characteristics are disrupted in patients with neurodegenerative diseases. New method: 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. Comparison with existing method: 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. (C) 2014 Elsevier B.V. All rights reserved.
Journal of Neuroscience Methods, 2014, Vol 235, p. 262-276
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; Journal Article; Research Support, Non-U.S. Gov't; Validation Studies; BIOCHEMICAL; NEUROSCIENCES; BEHAVIOR DISORDER; REM-SLEEP; DELAYED EMERGENCE; WAKEFULNESS; SWITCH; Parkinson's disease; Topic modeling; Automatic classification; neurodegeneration; periodic leg movement; Parkinson's disease nervous system disease diagnosis; REM sleep behavior disorder nervous system disease, behavioral and mental disorders; Primates Mammalia Vertebrata Chordata Animalia (Animals, Chordates, Humans, Mammals, Primates, Vertebrates) - Hominidae  human common middle age, aged female, male; 04500, Mathematical biology and statistical methods; 07004, Behavioral biology - Human behavior; 10515, Biophysics - Biocybernetics; 12504, Pathology - Diagnostic; 20506, Nervous system - Pathology; 21002, Psychiatry - Psychopathology, psychodynamics and therapy; 24500, Gerontology; Computational Biology; Human Medicine, Medical Sciences; data-driven modeling mathematical and computer techniques; electroencephalography EEG clinical techniques, diagnostic techniques; electrooculography EOG clinical techniques, diagnostic techniques; Lasso-regularized regression model mathematical and computer techniques; Methods and Techniques; Models and Simulations; Neurology