Vasquez-Correa, J.C., Orozco-Arroyave, J.R., Arora, R. et al. (12 more authors) (2017) Multi-view representation learning via gcca for multimodal analysis of Parkinson's disease. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 05-09 Mar 2017, New Orleans, LA, USA. IEEE , pp. 2966-2970. ISBN 9781509041176
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: | This paper has 15 authors. You can scroll the list below to see them all or them all.
|
Copyright, Publisher and Additional Information: | © 2017 IEEE. |
Keywords: | Parkinson’s disease; Multi-view learning; GCCA; Speech processing; Handwriting processing; Gait processing; UPDRS; Frenchay dysarthria assessment |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Aug 2017 14:42 |
Last Modified: | 19 Dec 2022 13:36 |
Published Version: | https://www.doi.org/10.1109/ICASSP.2017.7952700 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICASSP.2017.7952700 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:119848 |