Title: Jitter as a quantitative indicator of dysphonia in Parkinson's disease

Authors: Jennifer C. Saldanha; Malini Suvarna; Dayakshini Satish; Cynthia Santhmayor

Addresses: Department of Electronics and Communication, St. Joseph Engineering College, Vamanjoor, Mangaluru, India; Affiliated to: VTU Belagavi, India ' Department of Electronics and Communication, Tontadarya College of Engineering, Gadag, India; Affiliated to: VTU Belagavi, India ' Department of Electronics and Communication, St. Joseph Engineering College, Vamanjoor, Mangaluru, India; Affiliated to: VTU Belagavi, India ' Department of Speech, Father Muller College of Speech and Hearing, Kankanady, Mangalore, India

Abstract: A non-invasive way of diagnosing Parkinson's disease from speech signals is presented in this paper. A variety of frequency, amplitude, harmonicitynoise, and cepstral features are extracted from speech samples, resulting in a feature vector of 82 coefficients. k-nearest neighbours (k-NN) with k = 10 and artificial neural network (ANN) are applied to the dataset on individual and combined features to detect Parkinson's disease. The jitter feature obtained a maximum accuracy with both k-NN and ANN classifiers. k-NN outperformed ANN by obtaining a classification accuracy of 90% for jitter local features and 88.3% for combined features. The severity of the disease is assessed using multi-class classification, obtaining an overall accuracy of 83.6% and 82.4% for k-NN and ANN, respectively. The accuracy in detection is also verified on the dataset divided based on age and gender category. The results of the perceptual test proved that the predominant voice quality in Parkinson's disease is hoarse.

Keywords: Parkinson's disease; mel frequency cepstral coefficients; linear prediction cepstral coefficients; k-nearest neighbour; k-NN; multi-layer perceptron artificial neural network.

DOI: 10.1504/IJISTA.2023.131576

International Journal of Intelligent Systems Technologies and Applications, 2023 Vol.21 No.2, pp.199 - 228

Received: 28 Jan 2022
Accepted: 22 Aug 2022
Published online: 19 Jun 2023 *

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