Characterisation of movement disorder in parkinson's disease using evolutionary algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Lacy:2013:GECCOcomp,

• 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",
• title = "Characterisation of movement disorder in parkinson's disease using evolutionary algorithms",
• booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion",
• year = "2013",
• 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",
• keywords = "genetic algorithms, genetic programming",
• pages = "1479--1486",
• month = "6-10 " # jul,
• organisation = "SIGEVO",
• address = "Amsterdam, The Netherlands",
• DOI = "doi:10.1145/2464576.2482726",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• 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