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Automated Assessment of Bradykinesia and Dyskinesia in Parkinson's Disease

Article type: Research Article

Authors: Griffiths, Robert I. | Kotschet, Katya; | Arfon, Sian | Xu, Zheng Ming | Johnson, William | Drago, John; ; | Evans, Andrew | Kempster, Peter | Raghav, Sanjay; | Horne, Malcolm K.; ;

Affiliations: Florey Neuroscience Institutes, University of Melbourne, Parkville, Victoria, Australia | St Vincent's Hospital, Fitzroy, Victoria, Australia | Centre for Neurosciences, University of Melbourne, Parkville, Victoria, Australia | Compumedics, Abbotsford, Victoria, Australia | Royal Melbourne Hospital, Parkville, Victoria, Australia | Southern Health, Clayton, Victoria, Australia | Monash University, Clayton, Victoria, Australia

Note: [] Correspondence to: Prof. M.K. Horne, Florey Neuroscience Institutes, Melbourne Brain Centre, @ Genetics lane, University of Melbourne, Parkville, 3010 Victoria, Australia. Tel.: +61 9035 6589; Fax: +61 613 9035 3107; E-mail: [email protected]

Keywords: Parkinson's disease, bradykinesia, dyskinesia, spectral power, automated measurement, movement disorder

DOI: 10.3233/JPD-2012-11071

Journal: Journal of Parkinson's Disease, vol. 2, no. 1, pp. 47-55, 2012

Published: 2012
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Abstract

There is a need for objective measures of dyskinesia and bradykinesia of Parkinson's disease (PD) that are continuous throughout the day and related to levodopa dosing. The output of an algorithm that calculates dyskinesia and bradykinesia scores every two minutes over 10 days (PKG: Global Kinetics Corporation) was compared with conventional rating scales for PD in PD subjects. The algorithm recognises bradykinesia as movements made with lower acceleration and amplitude and with longer intervals between movement. Similarly the algorithm recognises dyskinesia as having movements of normal amplitude and acceleration but with shorter periods without movement. The distribution of the bradykinesia and dyskinesia scores from PD subjects differed from that of normal subjects. The algorithm predicted the clinical dyskinesia rating scale AIMS with a 95% margin of error of 3.2 units compared with the inter-rater 95% limits of agreement from 3 neurologists of −3.4 to +4.3 units. Similarly the algorithm predicted the UPDRS III score with a margin of error similar to the inter-rater limits of agreement. Improvement in scores in response to changes in medication could be assessed statistically in individual patients. This algorithm provides objective, continuous and automated assessment of the clinical features of bradykinesia and dyskinesia in PD.

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