Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity

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

Research output: Contribution to journalArticle

Abstract

The standard reference clinical score quantifying average Parkinson's disease (PD) symptom severity is the Unified Parkinson's Disease Rating Scale (UPDRS). At present, UPDRS is determined by the subjective clinical evaluation of the patient's ability to adequately cope with a range of tasks. In this study, we extend recent findings that UPDRS can be objectively assessed to clinically useful accuracy using simple, self-administered speech tests, without requiring the patient's physical presence in the clinic. We apply a wide range of known speech signal processing algorithms to a large database (approx. 6000 recordings from 42 PD patients, recruited to a six-month, multi-centre trial) and propose a number of novel, nonlinear signal processing algorithms which reveal pathological characteristics in PD more accurately than existing approaches. Robust feature selection algorithms select the optimal subset of these algorithms, which is fed into non-parametric regression and classification algorithms, mapping the signal processing algorithm outputs to UPDRS. We demonstrate rapid, accurate replication of the UPDRS assessment with clinically useful accuracy (about 2 UPDRS points difference from the clinicians' estimates, p < 0.001). This study supports the viability of frequent, remote, cost-effective, objective, accurate UPDRS telemonitoring based on self-administered speech tests. This technology could facilitate large-scale clinical trials into novel PD treatments.
Original languageEnglish
Pages (from-to)842-855
Number of pages14
JournalJournal of the Royal Society Interface
Volume8
Issue number59
Early online date17 Nov 2010
DOIs
Publication statusPublished - 6 Jun 2011

Bibliographical note

© 2010 The Royal Society

Keywords

  • nonlinear speech signal processing
  • nonlinear regression and classification
  • Parkinson's disease
  • telemedicine
  • unified Parkinson's disease rating scale

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