| Full text | |
| Author(s): |
Total Authors: 5
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| Affiliation: | [1] Univ Fed ABC, Ctr Math Comp & Cognit, Santo Andre - Brazil
[2] Univ Estadual Oeste Parana, Ctr Engn & Ciencias Exatas, Lab Bioinformat, Foz Do Iguacu - Brazil
[3] Univ Fed Sao Paulo, Inst Ciencia & Tecnol, Sao Jose Dos Campos - Brazil
[4] Univ Sao Paulo, Reference Ctr Cognit Disorders, Hosp Clin, Rua Arruda Alvim 206, Sao Paulo - Brazil
Total Affiliations: 4
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| Document type: | Journal article |
| Source: | CLINICAL NEUROPHYSIOLOGY; v. 128, n. 10, p. 2058-2067, OCT 2017. |
| Web of Science Citations: | 10 |
| Abstract | |
Objective: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among each other. Thus, feature selection (FS) algorithms are often considered to find relevant/non-redundant features.\& para;\& para;Objective: This study aimed to evaluate the relevance of FS approaches applied to Alzheimer's Disease (AD) EEG-based diagnosis and compare the selected features with previous clinical findings.\& para;\& para;Methods: Eight different FS algorithms were applied to EEG spectral measures from 22 AD patients and 12 healthy age-matched controls. The FS contribution was evaluated by considering the leave-one-subject-out accuracy of Support Vector Machine classifiers built in the datasets described by the selected features.\& para;\& para;Results: The Filtered Subset Evaluator technique achieved the best performance improvement both on a per-patient basis (91.18% of accuracy) and on a per-epoch basis (85.29 +/- 21.62%), after removing 88.76 +/- 1.12% of the original features. All algorithms found out that alpha and beta bands are relevant features, which is in agreement with previous findings from the literature.\& para;\& para;Conclusion: Biologically plausible EEG datasets could achieve improved accuracies with pre-processing FS steps.\& para;\& para;Significance: The results suggest that the FS and classification techniques are an attractive complementary tool in order to reveal potential biomarkers aiding the AD clinical diagnosis. (C) 2017 Published by Elsevier Ireland Ltd on behalf of International Federation of Clinical Neurophysiology. (AU) | |
| FAPESP's process: | 12/22608-8 - Use of data complexity measures in the support of supervised machine learning |
| Grantee: | Ana Carolina Lorena |
| Support Opportunities: | Research Grants - Young Investigators Grants |
| FAPESP's process: | 13/00506-1 - Time series, wavelets and functional data analysis |
| Grantee: | Pedro Alberto Morettin |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 13/10952-9 - Evaluation of the neural substrate of learning control of brain-computer interface in healthy subjects |
| Grantee: | Lucas Remoaldo Trambaiolli |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 13/10498-6 - Machine learning in neuroimaging: development of methods and clinical applications in psychiatric disorders |
| Grantee: | João Ricardo Sato |
| Support Opportunities: | Regular Research Grants |