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

Michael A. Lones*, Andrew J. Turner, Jane E. Alty, D. R Stuart Jamieson, Phillipa Duggan-Carter, Stephen L. Smith

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


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 e?ectiveness 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.

Original languageEnglish
Title of host publicationGECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Print)978-1-4503-2881-4
Publication statusPublished - 2014
Event16th Genetic and Evolutionary Computation Conference 2014 - Vancouver, Canada
Duration: 12 Jul 201416 Jul 2014


Conference16th Genetic and Evolutionary Computation Conference 2014
Abbreviated titleGECCO 2014


  • Classification
  • Fourier analysis
  • Genetic programming
  • Parkinson's disease
  • Pattern discovery
  • Time series analysis


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