Objective Assessment of Cognitive Impairment in Parkinson's Disease Using Evolutionary Algorithm
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Picardi:2017:evoApplications,
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author = "Chiara Picardi and Jeremy Cosgrove and
Stephen L. Smith and Stuart Jamieson and Jane E. Alty",
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title = "Objective Assessment of Cognitive Impairment in
{Parkinson's} Disease Using Evolutionary Algorithm",
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booktitle = "20th European Conference on the Applications of
Evolutionary Computation",
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year = "2017",
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editor = "Giovanni Squillero",
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series = "LNCS",
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volume = "10199",
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publisher = "Springer",
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pages = "109--124",
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address = "Amsterdam",
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month = "19-21 " # apr,
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organisation = "Species",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, SVM, ANN, Classification,
Parkinsons disease, Machine learning, Artificial
intelligence",
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DOI = "doi:10.1007/978-3-319-55849-3_8",
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abstract = "Parkinson's disease (PD) is a common and disabling
condition without cure. An early and accurate diagnosis
is important for monitoring the disease and managing
symptoms. Over time, the majority of patients with PD
develop cognitive impairment, which is diagnosed using
global tests of cognitive function or more detailed
neuropsychological assessment. This paper presents an
approach to detect PD and to discriminate different
degrees of PD cognitive impairment in an objective way,
considering a simple and non-invasive reach and grasp
task performed with the patient wearing sensor-enabled
data gloves recording movements in real-time. The PD
patients comprised three subgroups: 22 PD patients with
normal cognition (PD-NC), 23 PD patients with mild
cognitive impairment (PD-MCI) and 10 PD patients with
dementia (PDD). In addition, 30 age-matched healthy
subjects (Controls) were also measured. From the
experimental data, 25 kinematic features were extracted
with the aim of generating a classifier that is able to
discriminate not only between Controls and PD patients,
but also between the PD cognitive subgroups. The
technique used to find the best classifier was an
Evolutionary Algorithm - Cartesian Genetic Programming
(CGP), and this is compared with Support Vector Machine
(SVM) and Artificial Neural Network (ANN). In all
cases, the CGP classifiers were comparable with SVM and
ANN, and in some cases performed better. The results
are promising and show both the potential of the
computed features and of CGP in aiding PD diagnosis.",
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notes = "EvoApplications2017 held in conjunction with
EuroGP'2017, EvoCOP2017 and EvoMusArt2017
http://www.evostar.org/2017/cfp_evoapps.php.",
- }
Genetic Programming entries for
Chiara Picardi
Jeremy Cosgrove
Stephen L Smith
D R Stuart Jamieson
Jane E Alty
Citations