Objective Assessment of Cognitive Impairment in Parkinson's Disease Using Evolutionary Algorithm

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

@InProceedings{Picardi:2017:evoApplications,

• author = "Chiara Picardi and Jeremy Cosgrove and Stephen L. Smith and Stuart Jamieson and Jane E. Alty",
• title = "Objective Assessment of Cognitive Impairment in {Parkinson's} Disease Using Evolutionary Algorithm",
• booktitle = "20th European Conference on the Applications of Evolutionary Computation",
• year = "2017",
• editor = "Giovanni Squillero",
• series = "LNCS",
• volume = "10199",
• publisher = "Springer",
• pages = "109--124",
• address = "Amsterdam",
• month = "19-21 " # apr,
• organisation = "Species",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, SVM, ANN, Classification, Parkinsons disease, Machine learning, Artificial intelligence",
• DOI = "doi:10.1007/978-3-319-55849-3_8",
• 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.",
• 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