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

Athanasios Tsanas, Max A. Little, Patrick E. McSharry, Jennifer Spielman, Lorraine O. Ramig, Jennifer Spielman

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number6126094
Pages (from-to)1264-1271
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Issue number5
Publication statusPublished - May 2012

Bibliographical note

© 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.”


  • decision support tool
  • support vector machines
  • random forests
  • parkinson’s disease
  • feature selection (FS) , nonlinear speech signal processing


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