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 journalArticlepeer-review

    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

    Fingerprint

    Dive into the research topics of 'Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity'. Together they form a unique fingerprint.

    Cite this