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

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  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
  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

Genetic Programming entries for Chiara Picardi Jeremy Cosgrove Stephen L Smith D R Stuart Jamieson Jane E Alty