发表机构
University of North Texas; University of Tehran（北德克萨斯大学; 德黑兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用FBSE - ED和DWT两种时频方法生成脑电图信号图像，提出深度学习分类器预测rTMS抑郁症治疗结果，经实验验证该方法准确率高，优于多种模型，能促进早期预测和临床决策，且框架具有可解释性和高效性。
AI 中文摘要
重复经颅磁刺激（rTMS）是一种治疗重度抑郁症（MDD）的非侵入性疗法。本研究中，我们使用两种时频方法生成图像来表示脑电图信号：带欧几里得距离的傅里叶 - 贝塞尔级数展开（FBSE - ED）和离散小波变换（DWT）。我们提出了一种高效的深度学习分类器来预测rTMS抑郁症治疗的结果。使用一个私有rTMS数据库，采用10折交叉验证策略训练一个轻量级自定义卷积神经网络（CNN）。结果表明，FBSE - ED表示实现了93.60%的最高分类准确率，优于传统时频技术（DWT）。此外，所提出的带有FBSE - ED图像表示技术的架构比更复杂的脑电图特定深度学习模型（EEGNet、DeepConvNet、SleepEEGNet）高出3.62 - 10.72%，比预训练模型（Xception、DenseNet201和MobileNetV2）高出23.03 - 27.35%。利用另一个私有rTMS数据库进行更多实验以展示所提模型的鲁棒性。我们的结果表明，将先进的信号分解与深度学习相结合可以促进rTMS治疗反应的早期预测，并支持更有针对性的临床决策。所提出的框架具有可解释性、计算效率高，适合在现实世界的当地精神病诊所中部署。
英文摘要
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.
CommentsPresented at 8th International Conference on Recent Trends in Image Processing & Pattern Recognition (RTIP2R)
