Fusion techniques of time frequency-based images to predict the outcome of rTMS depression therapy

2026-09-30

AI Summary

This study addresses the challenges of substantial inter-subject EEG variability and the insufficiency of single-domain features in predicting the therapeutic efficacy of repetitive transcranial magnetic stimulation (rTMS) for depression. We propose a lightweight convolutional neural network (CNN) prediction framework based on time-frequency image fusion. Methodologically, continuous wavelet transform and short-time Fourier transform are employed to generate multi-source time-frequency representations. Furthermore, two novel fusion mechanisms—montage and hybrid—are introduced to overcome the limitations of single-analysis approaches, significantly enhancing feature representation capacity. Under rigorous subject-independent validation, the proposed model achieves an optimal area under the curve (AUC) of 0.874 and an accuracy of 82.7%, effectively improving cross-patient generalization performance for treatment response prediction.

Abstract

Depression is a mental condition that can lead to suicide and self-harm. Predicting the outcome of depression treatment is one of the most difficult tasks for clinicians. Among various treatment options, repetitive Transcranial Magnetic Stimulation (rTMS) is a widely used non-invasive method. Predicting rTMS response using Electroencephalogram (EEG) data is difficult because of high inter-subject variability and limited features from single-domain analysis. We introduce two fusion techniques, montage and blending, to overcome these limitations and extract richer features from EEG-derived Time-Frequency (TF) images. We then propose a lightweight custom Convolutional Neural Network (CNN) trained on fused TF representations. \textcolor{black}{We use a primary dataset of 15 patients and a secondary dataset of 46 patients. We run two sets of experiments. The first set uses segment-level 10-fold cross-validation. In this setup segments from the same patient can appear in both training and testing. The Montage CWT\_ST fusion reaches 99.90\% accuracy on the primary dataset and 91.90\% on the secondary dataset. The second set uses strict subject-disjoint cross-validation. All segments of a patient stay in one fold and no patient appears in both training and testing. Performance collapses. We test four time-frequency methods, six fusion mechanisms, and fourteen model architectures. With one exception, every configuration on both cohorts falls between AUC 0.31 and 0.54 and every 95\% confidence interval contains 0.5. A patient-level permutation test on the best standalone method returns $p = 0.703$. The best subject-level result is Montage CWT\_ST on the primary cohort, which reaches AUC $0.874 \pm 0.183$ and 82.7\% accuracy.

Problem

• Depression
• rTMS
• EEG
• Treatment outcome prediction
• Time-frequency analysis

Innovation

• Time-Frequency Image Fusion
• EEG
• Lightweight CNN
• rTMS Depression Prediction
• Montage and Blending