Evolving Classifiers to Recognise the Movement Characteristics of Parkinson's Disease Patients
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Lones:2014:ieeeTEC,
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author = "Michael Adam Lones and Stephen Leslie Smith and
Jane Elizabeth Alty and Stuart E. Lacy and
Katherine L. Possin and D. R. Stuart Jamieson and Andy M. Tyrrell",
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title = "Evolving Classifiers to Recognise the Movement
Characteristics of Parkinson's Disease Patients",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2014",
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volume = "18",
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number = "4",
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pages = "559--576",
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month = aug,
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keywords = "genetic algorithms, genetic programming, artificial
biochemical networks, Automated disease diagnosis, Time
series analysis, Classification",
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ISSN = "1089-778X",
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URL = "http://www-users.york.ac.uk/~mal503/common/papers/lones-tevc2013-PD.pdf",
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DOI = "doi:10.1109/TEVC.2013.2281532",
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size = "18 pages",
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abstract = "Parkinson's disease is a debilitating neurological
condition that affects approximately 1 in 500 people
and often leads to severe disability. To improve
clinical care, better assessment tools are needed that
increase the accuracy of differential diagnosis and
disease monitoring. we report how we have used
evolutionary algorithms to induce classifiers capable
of recognising the movement characteristics of
Parkinson's disease patients. These
diagnostically-relevant patterns of movement are known
to occur over multiple time scales. To capture this, we
used two different classifier architectures:
sliding-window genetic programming classifiers, which
model over-represented local patterns that occur within
time series data, and artificial biochemical networks,
computational dynamical systems that respond to
dynamical patterns occurring over longer time scales.
Classifiers were trained and validated using movement
recordings of 49 patients and 41 age-matched controls
collected during a recent clinical study. By combining
classifiers with diverse behaviours, we were able to
construct classifier ensembles with diagnostic
accuracies in the region of 95percent, comparable to
the accuracies achieved by expert clinicians. Further
analysis indicated a number of features of diagnostic
relevance, including the differential effect of
handedness and the over-representation of certain
patterns of acceleration.",
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notes = "Also known as \cite{6600775}",
- }
Genetic Programming entries for
Michael A Lones
Stephen L Smith
Jane E Alty
Stuart E Lacy
Katherine L Possin
D R Stuart Jamieson
Andrew M Tyrrell
Citations