Decoding EEG Signals to Predict SSRI Therapy Success in Depression Using Automated Tunable Q-Factor Wavelet Transform and Centered Correntropy

Abstract

Depression, a prevalent mental disorder, can have severe consequences if left untreated, including self-harm and suicide. Selective Serotonin Reuptake Inhibitors (SSRI) therapy is the first course of treatment for depression disorder. Accurate prediction of SSRI therapy outcomes could significantly assist medical professionals in tailoring treatment plans to individual subjects. Electroencephalography (EEG) signals, which reflect the brain's neural activity, offer a non-invasive avenue for such predictions. However, visual analysis of EEG signals is laborious and time-consuming, given their complex, nonlinear, and nonstationary nature. EEG signals are complex, nonlinear, and nonstationary. Consequently, EEG signals need to be decomposed into several sub-bands to extract detailed and representative information. Traditional manual filter bank design for decomposition risks information loss. To address this challenge, this study proposes an automated tunable-Q wavelet transform (ATQWT) framework for automatic signal decomposition, which aims to preserve critical information during analysis. The Starfish optimization algorithm (SFOA), a bio-inspired metaheuristic approach, is employed to optimize the parameters of ATQWT, facilitating the automatic selection of optimal tuning parameters to extract meaningful sub-bands and enhance signal reconstruction during synthesis. Centered correntropy is utilized to compute features from the sub-bands, and the most discriminative features are identified using a nearest neighbor algorithm. These features are then classified using a feedforward neural network, and a 10-fold cross-validation strategy is implemented to mitigate potential bias in the results. The proposed method achieves an outstanding classification accuracy of 99.36% in predicting SSRI therapy outcomes. Results show F4, P4, C4, Fp2, F8 and Fz are the most informative channels for predicting SSRI therapy outcomes. So, the right-lateralized prefrontal and parietal lobes are more involved in depression therapy. This approach holds significant potential for assisting medical teams in clinical settings to develop more personalized and effective therapy plans for subjects with depression. © 2025 IEEE.