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

Version 2 2025-01-15, 01:17
Version 1 2023-05-23, 15:00
conference contribution
posted on 2025-01-15, 01:17 authored by MA Lones, Jane AltyJane Alty, P Duggan-Carter, AJ Turner, DRS Jamieson, SL Smith
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.

History

Volume

16

Pagination

1321-1328

ISBN

978-1-4503-2881-4

Department/School

Wicking Dementia Research Education Centre

Publisher

ACM

Publication status

  • Published

Place of publication

Canada

Event title

2014 Annual Conference on Genetic and Evolutionary Computation (GECCO’14)

Event Venue

Vancouver, BC, Canada

Date of Event (Start Date)

2015-07-12

Date of Event (End Date)

2015-07-16

Rights statement

Copyright 2014 ACM

Socio-economic Objectives

220403 Artificial intelligence