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Classification and characterisation of movement patterns during levodopa therapy for parkinson's disease

Citation

Lones, MA and Alty, JE and Duggan-Carter, P and Turner, AJ and Jamieson, DRS and Smith, SL, Classification and characterisation of movement patterns during levodopa therapy for parkinson's disease, 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO'14), 12-16 July, Vancouver, BC, Canada, pp. 1321-1328. ISBN 978-1-4503-2881-4 (2015) [Refereed Conference Paper]

Copyright Statement

Copyright 2014 ACM

DOI: doi:10.1145/2598394.2609852

Abstract

Parkinson's disease is a chronic neurodegenerative condition that manifests clinically with various movement disorders. These are often treated with the dopamine-replacement drug levodopa. However, the dosage of levodopa must be kept as low as possible in order to avoid the drug's side effects, such as the involuntary, and often violent, muscle spasms called dyskinesia, or levodopa-induced dyskinesia. In this paper, we investigate the use of genetic programming for training classifiers that can monitor the effectiveness of levodopa therapy. In particular, we evolve classifiers that can recognise tremor and dyskinesia, movement states that are indicative of insufficient or excessive doses of levodopa, respectively. The evolved classifiers achieve clinically useful rates of discrimination, with AUC>0.9. We also find that temporal classifiers generally out-perform spectral classifiers. By using classifiers that respond to low-level features of the data, we identify the conserved patterns of movement that are used as a basis for classification, showing how this approach can be used to characterise as well as classify abnormal movement.

Item Details

Item Type:Refereed Conference Paper
Keywords:Parkinson's, computer, levodopa, movement analysis, artificial intelligence
Research Division:Biomedical and Clinical Sciences
Research Group:Neurosciences
Research Field:Neurology and neuromuscular diseases
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Alty, JE (Associate Professor Jane Alty)
ID Code:144192
Year Published:2015
Deposited By:Wicking Dementia Research and Education Centre
Deposited On:2021-04-27
Last Modified:2021-06-09
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