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

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  author =       "Michael A. Lones and Jane E. Alty and 
                 Phillipa Duggan-Carter and Andrew J. Turner and 
                 D. R. Stuart Jamieson and Stephen L. Smith",
  title =        "Classification and characterisation of movement
                 patterns during levodopa therapy for parkinson's
  booktitle =    "GECCO 2014 Workshop on Medical Applications of Genetic
                 and Evolutionary Computation (MedGEC)",
  year =         "2014",
  editor =       "Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1321--1328",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "",
  DOI =          "doi:10.1145/2598394.2609852",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  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
  notes =        "Also known as \cite{2609852} Distributed at

Genetic Programming entries for Michael A Lones Jane E Alty Phillipa Duggan-Carter Andrew James Turner D R Stuart Jamieson Stephen L Smith