Predicting Depression Therapy Outcomes Using EEG-Derived Amplitude Polar Maps

초록

Background/Objectives: Depression is a mental disorder that can lead to self-harm or suicidal thoughts if left untreated. Psychiatrists often face challenges in identifying the most effective courses of treatment for patients with depression. Two widely recommended depression-related therapies are selective serotonin reuptake inhibitors (SSRIs) and repetitive transcranial magnetic stimulation (rTMS). However, their response rates are approximately 50%, which is relatively low. This study introduces a computer-aided decision (CAD) system designed to determine the effectiveness of depression therapies and recommends the most appropriate treatments for patients. Methods: Each channel of the EEG is plotted in two-dimensional (2D) space via a novel technique called the amplitude polar map (APM). In each channel, the 2D plot of APM is utilized to extract distinctive features via the binary pattern of five successive lines method. The extracted features from each channel are fused to generalize the pattern of EEG signals. The most relevant features are selected via the neighborhood component analysis algorithm. The chosen features are input into a simple feed-forward neural network architecture to classify the EEG signal of a depressed patient into either a respondent to depression therapies or not. The 10-fold cross-validation strategy is employed to ensure unbiased results. Results: The results of our proposed CAD system show accuracy rates of 98.06% and 97.19% for predicting the outcomes of SSRI and rTMS therapies, respectively. In SSRI predictions, prefrontal and parietal channels such as F7, Fz, Fp2, P4, and Pz were the most informative, reflecting brain regions involved in emotional regulation and executive function. In contrast, rTMS prediction relied more on frontal, temporal, and occipital channels such as F4, O2, T5, T3, Cz, and T6, indicating broader network modulation via neuromodulation. Conclusions: The proposed CAD framework holds considerable promise as a clinical decision-support tool, assisting mental health professionals in identifying the most suitable therapeutic interventions for individuals with depression.

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