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Deep learning model predicts rTMS depression therapy outcomes with 93.6% accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel deep learning model to predict the effectiveness of repetitive transcranial magnetic stimulation (rTMS) therapy for depression. By converting electroencephalography (EEG) signals into images using Fourier-Bessel Series Expansion with Euclidean Distance (FBSE-ED), the proposed Convolutional Neural Network (CNN) achieved a 93.60% classification accuracy. This approach outperformed existing EEG-specific and pre-trained models, suggesting its potential for targeted clinical decision-making and real-world deployment in psychiatric clinics. AI

IMPACT Could enable more precise and timely treatment decisions for depression patients undergoing rTMS therapy.

RANK_REASON Academic paper detailing a novel deep learning model for a specific medical application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning model predicts rTMS depression therapy outcomes with 93.6% accuracy

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Academic paper detailing a novel deep learning model for a specific medical application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Wael Korani, Md Fahimul Kabir Chowdhury, Sadam AlQadi, Priyan Malarvizhi kumar, Reza Rostami, Reza Kazemi ·

    Predicting the Outcome of rTMS Depression Therapy using EEG Signals and CNN

    arXiv:2607.22776v1 Announce Type: new 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 Ex…

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