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

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@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