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 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 Denmark9 Copenhagen University Hospital
Patients suffering from the sleep disorder idiopathic rapid-eye-movement sleep behavior disorder (iRBD) have been observed to be in high risk of developing Parkinson's disease (PD). This makes it essential to analyze them in the search for PD biomarkers. This study aims at classifying patients suffering from iRBD or PD based on features reflecting eye movements (EMs) during sleep. A Latent Dirichlet Allocation (LDA) topic model was developed based on features extracted from two electrooculographic (EOG) signals measured as parts in full night polysomnographic (PSG) recordings from ten control subjects. The trained model was tested on ten other control subjects, ten iRBD patients and ten PD patients, obtaining a EM topic mixture diagram for each subject in the test dataset. Three features were extracted from the topic mixture diagrams, reflecting “certainty”, “fragmentation” and “stability” in the timely distribution of the EM topics. Using a Naive Bayes (NB) classifier and the features “certainty” and “stability” yielded the best classification result and the subjects were classified with a sensitivity of 95 %, a specificity of 80% and an accuracy of 90 %. This study demonstrates in a data-driven approach, that iRBD and PD patients may exhibit abnorm form and/or timely distribution of EMs during sleep.
2013 35th Annual International Conference of the Ieee Engineering in Medicine and Biology Society Conference Proceedings, 2013, Vol 2013, p. 441-444
Bayes methods; diseases; electro-oculography; feature extraction; medical disorders; medical signal processing; signal classification; sleep; Engineered Materials, Dielectrics and Plasmas
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2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013