Classification of major depressive disorder subjects using Pre-rTMS electroencephalography data with support vector machine approach
The combination of repetitive transcranial magnetic stimulation (rTMS) and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. Using pre-treatment cordance, a relatively new quantitative EEG method combining complementary information from absolut...
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Published in | 2014 Science and Information Conference pp. 410 - 414 |
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Main Authors | , , , |
Format | Conference Proceeding Journal Article |
Language | English |
Published |
The Science and Information (SAI) Organization
01.08.2014
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Subjects | |
Online Access | Get full text |
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Summary: | The combination of repetitive transcranial magnetic stimulation (rTMS) and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. Using pre-treatment cordance, a relatively new quantitative EEG method combining complementary information from absolute and relative power of EEG spectra, 55 major depression disorder (MDD) subjects were classified into responder or non-responder classes. In order to predict the response of rTMS treatment, support vector machine (SVM) based classification was carried out on pre-treatment cordance and the classification performance was evaluated using 6, 8 and 10-fold cross-validation (CV). Promising findings indicate that it is possible to classify rTMS treatment responders with 85.45% overall accuracy with a sensitivity of 82.35% and 0.925 area under receiver operating characteristics (ROC) curve value. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Conference-1 ObjectType-Feature-3 content type line 23 SourceType-Conference Papers & Proceedings-2 |
ISBN: | 0989319334 9780989319331 |
DOI: | 10.1109/SAI.2014.6918220 |