Identifying Patients with Poststroke Mild Cognitive Impairment by Pattern Recognition of Working Memory Load-Related ERP

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Li:2013:CMMM,
  author =       "Xiaoou Li and Yuning Yan and Wenshi Wei",
  title =        "Identifying Patients with Poststroke Mild Cognitive
                 Impairment by Pattern Recognition of Working Memory
                 Load-Related {ERP}",
  journal =      "Computational and Mathematical Methods in Medicine",
  year =         "2013",
  pages =        "Article ID 658501",
  month =        oct # "~23",
  keywords =     "genetic algorithms, genetic programming, GP, SVM",
  bibsource =    "OAI-PMH server at www.ncbi.nlm.nih.gov",
  language =     "en",
  oai =          "oai:pubmedcentral.nih.gov:3819888",
  publisher =    "Hindawi Publishing Corporation",
  URL =          "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3819888",
  URL =          "http://dx.doi.org/10.1155/2013/658501",
  size =         "10 pages",
  abstract =     "The early detection of subjects with probable
                 cognitive deficits is crucial for effective appliance
                 of treatment strategies. This paper explored a
                 methodology used to discriminate between evoked related
                 potential signals of stroke patients and their matched
                 control subjects in a visual working memory paradigm.
                 The proposed algorithm, which combined independent
                 component analysis and orthogonal empirical mode
                 decomposition, was applied to extract independent
                 sources. Four types of target stimulus features
                 including P300 peak latency, P300 peak amplitude, root
                 mean square, and theta frequency band power were
                 chosen. Evolutionary multiple kernel support vector
                 machine (EMK-SVM) based on genetic programming was
                 investigated to classify stroke patients and healthy
                 controls. Based on 5-fold cross-validation runs,
                 EMK-SVM provided better classification performance
                 compared with other state-of-the-art algorithms.
                 Comparing stroke patients with healthy controls using
                 the proposed algorithm, we achieved the maximum
                 classification accuracies of 91.76percent and
                 82.23percent for 0-back and 1-back tasks, respectively.
                 Overall, the experimental results showed that the
                 proposed method was effective. The approach in this
                 study may eventually lead to a reliable tool for
                 identifying suitable brain impairment candidates and
                 assessing cognitive function.",
}

Genetic Programming entries for Xiaoou Li Yuning Yan Wenshi Wei

Citations