Classification and characterisation of movement patterns during levodopa therapy for parkinson's disease
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Lones:2014:GECCOcomp,
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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",
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title = "Classification and characterisation of movement
patterns during levodopa therapy for parkinson's
disease",
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booktitle = "GECCO 2014 Workshop on Medical Applications of Genetic
and Evolutionary Computation (MedGEC)",
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year = "2014",
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editor = "Stephen L. Smith and Stefano Cagnoni and
Robert M. Patton",
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isbn13 = "978-1-4503-2881-4",
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keywords = "genetic algorithms, genetic programming",
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pages = "1321--1328",
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month = "12-16 " # jul,
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organisation = "SIGEVO",
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address = "Vancouver, BC, Canada",
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URL = "http://doi.acm.org/10.1145/2598394.2609852",
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DOI = "doi:10.1145/2598394.2609852",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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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.",
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notes = "Also known as \cite{2609852} Distributed at
GECCO-2014.",
- }
Genetic Programming entries for
Michael A Lones
Jane E Alty
Phillipa Duggan-Carter
Andrew James Turner
D R Stuart Jamieson
Stephen L Smith
Citations