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

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

@InProceedings{Lones:2014:GECCOcomp,

• 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 disease",
• 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 = "http://doi.acm.org/10.1145/2598394.2609852",
• 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 movement.",
• 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