Automated detection of Parkinson's disease using minimum average maximum tree and singular value decomposition method with vowels

• Autorzy: Tuncer Turker , Dogan Sengul , Acharya Udyavara Rajendra

Identyfikatory

DOI

10.1016/j.bbe.2019.05.006

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

Parkinson's disease recognition; minimum average maximum tree; singular value decomposition; machine learning; signal processing

PL

rozpoznanie choroby Parkinsona; rozkład na wartości szczególne; uczenie maszynowe; przetwarzanie sygnału

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 1
• Strony: 211--220
• Opis fizyczny: Bibliogr. 47 poz., rys., tab., wykr.

Twórcy

autor

Tuncer Turker

• Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey

autor

Dogan Sengul

• Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey

autor

Acharya Udyavara Rajendra

• Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Science (SUSS), Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).