用脑电图和卷积网络预测抑郁症经颅磁刺激疗效，准确率达93.6%。
Predicting the Outcome of rTMS Depression Therapy using EEG Signals and CNN
- 将脑电信号转为图像，用轻量CNN模型分类预测治疗效果。
- 基于FBSE-ED方法的模型准确率93.6%，优于传统与复杂模型。
- 模型简洁高效，适合临床部署，可辅助医生早期判断疗效。
重复经颅磁刺激（rTMS）是治疗重度抑郁症（MDD）的一种非侵入性疗法。本研究采用傅里叶-贝塞尔级数展开结合欧氏距离（FBSE-ED）和离散小波变换（DWT）两种时频方法，将脑电信号转换为图像表示。提出一种轻量级自定义卷积神经网络（CNN），通过10折交叉验证在私有rTMS数据库上训练，避免结果偏差。结果显示，FBSE-ED表示法达到最高分类准确率93.60%，优于传统时频方法（DWT）。该架构相比EEGNet、DeepConvNet、SleepEEGNet等专用深度模型提升3.62%-10.72%，较Xception、DenseNet201、MobileNetV2等预训练模型提升23.03%-27.35%。为进一步验证鲁棒性，使用另一组私有rTMS数据库进行测试。结果表明，结合先进信号分解与深度学习，可实现rTMS治疗反应的早期预测，支持更精准的临床决策。所提框架具备可解释性、计算高效，适用于真实世界精神科诊所部署。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。