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Computational and Mathematical Methods in MedicineVolume 2013 2013, Article ID 658501, 10 pages

Research Article

Shanghai Medical Instrumentation College, Shanghai 200093, China

School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Department of Neurology, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China

Received 6 June 2013; Revised 31 August 2013; Accepted 5 September 2013

Academic Editor: Shahram Shirani

Copyright © 2013 Xiaoou Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 91.76% and 82.23% 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.

Autor: Xiaoou Li, Yuning Yan, and Wenshi Wei



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