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MULTI-VIEW REPRESENTATION LEARNING VIA GCCA FOR MULTIMODAL ANALYSIS OF PARKINSON'S DISEASE

Citation Author(s):
J. C. Vásquez-Correa, J. R. Orozco-Arroyave, R. Arora, E. Nöth, N. Dehak, H. Christensen, F. Rudzicz, T. Bocklet, M. Cernak, H. Chinaei, J. Hannink, Phani S. Nidadavolu, M. Yancheva, A. Vann, N. Vogler
Submitted by:
Juan Vasquez-Correa
Last updated:
28 February 2017 - 3:28pm
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Juan Camilo Vasquez-Correa
Paper Code:
3000
 

Information from different bio--signals such as speech, handwriting, and gait have been used to monitor the state of Parkinson's disease (PD) patients, however, all the multimodal bio--signals may not always be available. We propose a method based on multi-view representation learning via generalized canonical correlation analysis (GCCA) for learning a representation of features extracted from handwriting and gait that can be used as a complement to speech--based features. Three different problems are addressed: classification of PD patients vs. healthy controls, prediction of the neurological state of PD patients according to the UPDRS score, and the prediction of a modified version of the Frenchay dysarthria assessment (m-FDA). According to the results, the proposed approach is suitable to improve the results in the addressed problems, specially in the prediction of the UPDRS, and m-FDA scores.

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