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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1264-1271
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Volume59
Issue number5
DOIs
Publication statusPublished - 9 Jan 2012

Keywords

  • Decision support tool
  • feature selection (FS)
  • Parkinson's disease (PD)
  • nonlinear speech signal processing
  • random forests (RF)
  • support vector machines (SVM)
  • TIME-SERIES ANALYSIS
  • VECTOR MACHINES
  • PARAMETERS
  • FREQUENCY
  • DYSPHONIA
  • SELECTION
  • RELEVANCE
  • MODELS
  • LASSO

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