Tuncer, Turker and Dogan, Sengul and Acharya, Udyavara Rajendra (2020) Automated detection of Parkinson's disease using minimum average maximum tree and singular value decomposition method with vowels. Biocybernetics and Biomedical Engineering, 40 (1). pp. 211-220. ISSN 0208-5216, DOI https://doi.org/10.1016/j.bbe.2019.05.006.
Full text not available from this repository.Abstract
In this study, a novel method to automatically detect Parkinson's disease (PD) using vowels is proposed. A combination of minimum average maximum (MAMa) tree and singular value decomposition (SVD) are used to extract the salient features from the voice signals. A novel feature signal is constructed from 3 levels of MAMa tree in the preprocessing phase. The SVD operator is applied to the constructed signal for feature extraction. Then 50 most distinctive features are selected using relief feature selection technique. Finally, k nearest neighborhood (KNN) with 10-fold cross validation is used for the classification. We have achieved the highest classification accuracy rate of 92.46% using vowels with KNN classifier. The dataset used consists of 3 vowels for each person. To obtain individual results, post processing step is performed and best result of 96.83% is obtained with KNN classifier. The proposed method is ready to be tested with huge database and can aid the neurologists in the diagnosis of PD using vowels. (c) 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Parkinson's disease recognition; Minimum average maximum tree; Singular value decomposition; Machine learning; Signal processing |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 20 May 2023 03:41 |
| Last Modified: | 20 May 2023 03:41 |
| URI: | http://eprints.um.edu.my/id/eprint/37196 |
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