Predicting the Outcome of rTMS Depression Therapy using EEG Signals and CNN
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Computer Science > Machine Learning
arXiv:2607.22776 (cs)
[Submitted on 24 Jul 2026]
Title:Predicting the Outcome of rTMS Depression Therapy using EEG Signals and CNN
Authors:Wael Korani, Md Fahimul Kabir Chowdhury, Sadam AlQadi, Priyan Malarvizhi kumar, Reza Rostami, Reza Kazemi
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Abstract:Repetitive transcranial magnetic stimulation (rTMS) is a non invasive therapy for Major Depressive Disorder (MDD). In this study, we generate images using two time frequency methods to represent EEG signals: Fourier-Bessel Series Expansion with Euclidean Distance (FBSE-ED) and Discrete Wavelet Transform (DWT). We propose an efficient deep learning classifier to predict the outcome of rTMS depression therapy. In this study, we use a private rTMS databases to train a lightweight custom Convolutional Neural Network (CNN) using 10-fold cross validation strategy in order to avoid any bias in our results. The results show that the FBSE-ED representation achieves the highest classification accuracy of 93.60\%, outperforming traditional time-frequency technique (DWT). In addition, the proposed architecture with FBSE-ED image representation technique outperforms more complex EEG-Specific deep learning models (EEGNet, DeepConvNet, SleepEEGNet) by 3.62-10.72% and pretrained models (Xception, DenseNet201, and MobileNetV2) by 23.03-27.35%. For more experiments, we utilize another private rTMS database as test database to show the robustness of the proposed model. Our results suggest that integrating advanced signal decomposition with deep learning can facilitate early prediction of rTMS treatment response and support more targeted clinical decision-making. The proposed framework is interpretable, computationally efficient, and well-suited for deployment in real-world local psychiatric clinics.
| Comments: | Presented at 8th International Conference on Recent Trends in Image Processing & Pattern Recognition (RTIP2R) |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.22776 [cs.LG] |
| (or arXiv:2607.22776v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.22776
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Md Fahimul Kabir Chowdhury [view email][v1] Fri, 24 Jul 2026 08:23:26 UTC (1,944 KB)
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View a PDF of the paper titled Predicting the Outcome of rTMS Depression Therapy using EEG Signals and CNN, by Wael Korani and 5 other authors
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