Wavelet-Based Method for Parkinson's Detection from Voice Signal
Abstract
Parkinson's disease (PD) is a brain disorder that causes speech and communication problems. The speech
issues are often described as slow speech and difficulty with articulation because Jaw muscles don’t
move with enough strength. Mainly, clinical experts make a voice assessment to analyze voice signals, to
detect PD. In this paper, we designed an intelligent model based on discrete wavelet transform (DWT) for
the detection of Parkinson's. Firstly, the voice signals are passed through DWT. The suggested DWTbased
model is then used to extract a collection of statistical features from voice data. The extracted
features are evaluated, and the significant ones are chosen. The features that have been chosen are fed
into a least squares support vector machine (LS-SVM), as well as another four classifiers K-nearest
neighbor (KNN), bagged tree, SVM (support vector machine), and k means for the comparison. The
proposed model is evaluated using a publicly available dataset named UCI machine learning repository.
Several tests were carried out, and the findings revealed that the proposed framework can classify voice
signals with a100% accuracy in the LS-SVM technique
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