Identifying Patients with Poststroke Mild Cognitive Impairment by Pattern Recognition of Working Memory Load-Related ERP
Joint Authors
Wei, Wenshi
Yan, Yuning
Li, Xiaoou
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-10-23
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Li, Xiaoou& Yan, Yuning& Wei, Wenshi. 2013. Identifying Patients with Poststroke Mild Cognitive Impairment by Pattern Recognition of Working Memory Load-Related ERP. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-488905
Modern Language Association (MLA)
Li, Xiaoou…[et al.]. Identifying Patients with Poststroke Mild Cognitive Impairment by Pattern Recognition of Working Memory Load-Related ERP. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-488905
American Medical Association (AMA)
Li, Xiaoou& Yan, Yuning& Wei, Wenshi. Identifying Patients with Poststroke Mild Cognitive Impairment by Pattern Recognition of Working Memory Load-Related ERP. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-488905
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-488905