Voice Assessments for Detecting Patients with Parkinson’s Diseases in Different Stages

Elmehdi Benmalek, Jamal Elmhamdi, Abdelilah Jilbab


Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to detect patients with Parkinson’s disease (PD). So we have computed 19 dysphonia measures from sustained vowels collected from 375 voice samples from healthy and people suffer from PD. All the features are analysed and the more relevant ones are selected by the Principal component analysis (PCA) to classify the subjects in 4 classes according to the UPDRS (unified Parkinson’s disease Rating Scale) score. We used k-folds cross validation method with (k=4) validation scheme; 75% for training and 25% for testing, along with the Support Vector Machines (SVM) with its different types of kernels. The best result obtained was 92.5% using the PCA and the linear SVM.


classification, parkinson’s disease, PCA, SVM, voice features,

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DOI: http://doi.org/10.11591/ijece.v8i6.pp4265-4271

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578