Characterisation of movement disorder in parkinson's disease using evolutionary algorithms
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Lacy:2013:GECCOcomp,
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author = "Stuart E. Lacy and Michael A. Lones and
Stephen L. Smith and Jane E. Alty and D. R. Stuart Jamieson and
Katherine L. Possin and Norbert Schuff",
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title = "Characterisation of movement disorder in parkinson's
disease using evolutionary algorithms",
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booktitle = "GECCO '13 Companion: Proceeding of the fifteenth
annual conference companion on Genetic and evolutionary
computation conference companion",
-
year = "2013",
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editor = "Christian Blum and Enrique Alba and
Thomas Bartz-Beielstein and Daniele Loiacono and
Francisco Luna and Joern Mehnen and Gabriela Ochoa and
Mike Preuss and Emilia Tantar and Leonardo Vanneschi and
Kent McClymont and Ed Keedwell and Emma Hart and
Kevin Sim and Steven Gustafson and
Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and
Nikolaus Hansen and Olaf Mersmann and Petr Posik and
Heike Trautmann and Muhammad Iqbal and Kamran Shafi and
Ryan Urbanowicz and Stefan Wagner and
Michael Affenzeller and David Walker and Richard Everson and
Jonathan Fieldsend and Forrest Stonedahl and
William Rand and Stephen L. Smith and Stefano Cagnoni and
Robert M. Patton and Gisele L. Pappa and
John Woodward and Jerry Swan and Krzysztof Krawiec and
Alexandru-Adrian Tantar and Peter A. N. Bosman and
Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and
David L. Gonzalez-Alvarez and
Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and
Kenneth Holladay and Tea Tusar and Boris Naujoks",
-
isbn13 = "978-1-4503-1964-5",
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keywords = "genetic algorithms, genetic programming",
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pages = "1479--1486",
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month = "6-10 " # jul,
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organisation = "SIGEVO",
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address = "Amsterdam, The Netherlands",
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DOI = "doi:10.1145/2464576.2482726",
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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 devastating illness with no
currently available cure. As the population ages, the
disease becomes more common with a large financial cost
to society. A rapid and accurate diagnosis, as well as
practical monitoring methods are essential for managing
the disease as best as possible. This paper discusses
two approaches to discriminating movement data between
healthy controls or Parkinson's Disease patients. One
is a standard statistical analysis, influenced by prior
work into classifying patients. The other is a
programmatic expression evolved using genetic
programming, which is trained to observe differences in
specific motion segments, rather than using arbitrary
windows of a full data series. The performance of the
statistical analysis method is relatively high, but it
still cannot discriminate as well as the evolved
classifier. This study compares favourably to previous
work, highlighting the usefulness of analysing a
successful classifier to influence design decisions for
future work. Examination of the evolved programmatic
expressions that had high discriminatory ability
provided useful insight into how Parkinson's Disease
patients and healthy subjects have differing movement
characteristics. This could be used to inform future
research into the physiology of repetitive motions in
Parkinson's Disease patients.",
-
notes = "Also known as \cite{2482726} Distributed at
GECCO-2013.",
- }
Genetic Programming entries for
Stuart E Lacy
Michael A Lones
Stephen L Smith
Jane E Alty
D R Stuart Jamieson
Katherine L Possin
Norbert Schuff
Citations