A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder

highlights abstract Objective: The problem of identifying, in advance, the most effective treatment agent for various psychi- atric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD). Methods: A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of fac- tor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a ''leave-n-out'' randomized permutation cross-validation pro- cedure. Results: A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval (75%, 100%). Conclusions: These results indicate that the proposed ML method holds considerable promise in predict- ing the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treat- ment EEG. Significance: The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs.