Using pre-treatment EEG data to predict response to SSRI treatment for MDD

• Published in: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology 2010-01, p.6103-6106
• Main Authors: Khodayari-Rostamabad, Ahmad, Reilly, James P., Hasey, Gary, de Bruin, Hubert, MacCrimmon, Duncan
• Format: Article
• Language: English
• Subjects: Antidepressants; Coherence; Electrodes; Electroencephalography; Feature extraction; Indexes; Training
• ISSN: 1094-687X; 1558-4615
• DOI: 10.1109/IEMBS.2010.5627823

The problem of identifying in advance the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we propose a machine learning (ML) methodology to predict the response to a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD), using pre-treatment electroencephalograph (EEG) measurements. The proposed feature selection technique is a modification of the method of Peng et al [10] that is based on a Kullback-Leibler (KL) distance measure. The classifier was realized as a kernelized partial least squares regression procedure, whose output is the predicted response. A low-dimensional kernelized principal component representation of the feature space was used for the purposes of visualization and clustering analysis. The overall method was evaluated using an 11-fold nested cross-validation procedure for which over 85% average prediction performance is obtained. The results indicate that ML methods hold considerable promise in predicting the efficacy of SSRI antidepressant therapy for major depression.