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
针对rTMS抑郁症疗效预测，提出时频图像拼接与混合融合技术及轻量级CNN，在片段级验证中表现优异，但严格受试者分离下性能崩溃，揭示评估方法的关键影响。
AI 中文摘要
抑郁症是一种可能导致自杀和自残的精神疾病。预测抑郁症治疗结果是临床医生面临的最困难任务之一。在各种治疗方案中，重复经颅磁刺激（rTMS）是一种广泛使用的非侵入性方法。由于受试者间差异大且单域分析特征有限，使用脑电图（EEG）数据预测rTMS反应较为困难。我们引入了两种融合技术——拼接（montage）和混合（blending），以克服这些限制并从EEG衍生的时频（TF）图像中提取更丰富的特征。随后，我们提出了一种轻量级定制卷积神经网络（CNN），在融合的TF表示上进行训练。我们使用包含15名患者的主要数据集和包含46名患者的次要数据集。我们进行了两组实验。第一组使用片段级10折交叉验证，在此设置中，同一患者的片段可能同时出现在训练集和测试集中。Montage CWT_ST融合在主要数据集上达到99.90%的准确率，在次要数据集上达到91.90%。第二组使用严格的受试者分离交叉验证，每个患者的所有片段保留在一个折中，且没有患者同时出现在训练集和测试集中。性能出现崩溃。我们测试了四种时频方法、六种融合机制和十四种模型架构。除一个例外，两个队列上的所有配置的AUC均在0.31至0.54之间，且每个95%置信区间都包含0.5。对最佳独立方法进行的患者级置换检验返回p = 0.703。最佳受试者级结果是主要队列上的Montage CWT_ST，其AUC达到0.874 ± 0.183，准确率为82.7%。
英文摘要
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.
CommentsPublished in the Biomedical Signal Processing and Control
DOI：10.1016/j.bspc.2026.111375
