arXiv：cs.LG（机器学习，全量分类）· Wael Korani, Md Fahimul Kabir Chowdhury, Mohammed Aledhari, Reza Rostami, Reza Kazemi·· 15 小时前AI 评分33
基于时频图像融合技术预测 rTMS 抑郁症治疗效果
Fusion techniques of time frequency-based images to predict the outcome of rTMS depression therapy
AI 导读
研究提出 montage 和 blending 两种融合技术，从 EEG 时频图像中提取更丰富特征，并用轻量级定制 CNN 训练。
正文
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.
| Comments: | Published in the Biomedical Signal Processing and Control |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.00380 [cs.LG] |
| (or arXiv:2610.00380v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00380 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.1016/j.bspc.2026.111375
DOI(s) linking to related resources |
Submission history
From: Md Fahimul Kabir Chowdhury [view email]
[v1]
Wed, 30 Sep 2026 08:50:26 UTC (969 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org