Detection of a Sleep Disorder Predicting Parkinson’s Disease

TY - GEN

T1 - Detection of a Sleep Disorder Predicting Parkinson’s Disease

AU - Hansen, Ingeborg H

AU - Marcussen, Mikkel

AU - Christensen, Gitte Julie

AU - Jennum, Poul

AU - Sorensen, Helge B D

PY - 2013

Y1 - 2013

N2 - Idiopathic rapid eye-movement (REM) sleep behavior disorder (iRBD) has been found to be a strong early predictor for later development into Parkinson's disease (PD). iRBD is diagnosed by polysomnography but the manual evaluation is laborious, why the aims of this study are to develop supportive methods for detecting iRBD from electroencephalo-graphic (EEG) signals recorded during REM sleep. This method classified subjects from their EEG similarity with the two classes iRBD patients and control subjects. The feature sets used for classifying subjects were based on the relative powers of the EEG signals in different frequency bands. The classification was based on the fast and classical K-means and Bayesian classifiers. With a subject-specific re-scaling of the feature set and the use of a Bayesian classifier the performance reached 90% in both sensitivity and specificity. For the purpose of reducing the feature count, the features were evaluated with the statistical Smith-Satterthwaite test and by using sequential forward selection a well-performing feature subset was found which contained only five features, while attaining a sensitivity and a specificity of both 80 %.

AB - Idiopathic rapid eye-movement (REM) sleep behavior disorder (iRBD) has been found to be a strong early predictor for later development into Parkinson's disease (PD). iRBD is diagnosed by polysomnography but the manual evaluation is laborious, why the aims of this study are to develop supportive methods for detecting iRBD from electroencephalo-graphic (EEG) signals recorded during REM sleep. This method classified subjects from their EEG similarity with the two classes iRBD patients and control subjects. The feature sets used for classifying subjects were based on the relative powers of the EEG signals in different frequency bands. The classification was based on the fast and classical K-means and Bayesian classifiers. With a subject-specific re-scaling of the feature set and the use of a Bayesian classifier the performance reached 90% in both sensitivity and specificity. For the purpose of reducing the feature count, the features were evaluated with the statistical Smith-Satterthwaite test and by using sequential forward selection a well-performing feature subset was found which contained only five features, while attaining a sensitivity and a specificity of both 80 %.

U2 - 10.1109/embc.2013.6610868

DO - 10.1109/embc.2013.6610868

M3 - Conference article

C2 - 24111055

SN - 0589-1019

VL - 2013

SP - 5793

EP - 5796

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

ER -