Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease

Tsanas, Athanasios, Little, Max A., McSharry, Patrick E., Spielman, Jennifer, Ramig, Lorraine O. and Spielman, Jennifer (2012). Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease. IEEE Transactions on Biomedical Engineering, 59 (5), pp. 1264-1271.

Abstract

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.

Publication DOI: https://doi.org/10.1109/TBME.2012.2183367
Divisions: Engineering & Applied Sciences > Mathematics
Additional Information: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Uncontrolled Keywords: decision support tool,support vector machines,random forests,parkinson’s disease,feature selection (FS) , nonlinear speech signal processing,Biomedical Engineering
Full Text Link: http://ieeexplo ... rnumber=6126094
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2012-05
Authors: Tsanas, Athanasios
Little, Max A. ( 0000-0002-1507-3822)
McSharry, Patrick E.
Spielman, Jennifer
Ramig, Lorraine O.
Spielman, Jennifer

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