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

Tuncer Turker, Dogan Sengul, Acharya Udyavara Rajendra

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 1

211--220

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.

The interference spectrum extraction of a gait characteristics data record

Chandzlik S., Piecha J.

Journal of Medical Informatics & Technologies

2004

Vol. 7

KB23--32

The paper shows several aspects of the gait data record analysis describing neurological diseases. The diagnosis of the gait abnormalities concerns interferences level of the patient physiological records. The disease source and level can be classified by the relevant interference functions. These functions were used for artificial records creation to multiply the necessary set of data needed for neural network training.