用新型卷积网络提升脑电图抑郁诊断准确率
QuanvNeXt: An end-to-end quanvolutional neural network for EEG-based detection of major depressive disorder
- 设计跨残差块，增强特征多样性与关联性
- 在两个数据集上准确率达93.1%，AUC达97.2%
- 模型预测可信且可解释，适合临床辅助诊断
本研究提出QuanvNeXt，一种端到端全量化卷积神经网络，用于基于脑电图（EEG）的重度抑郁症（MDD）诊断。该模型引入新颖的跨残差块，在保持参数效率的同时降低特征同质性，强化跨特征关系。我们在两个开源数据集上评估了QuanvNeXt，平均准确率达到93.1%，平均AUC-ROC为97.2%，优于InceptionTime等先进基线模型（91.7%准确率，95.9% AUC-ROC）。在不同高斯噪声水平下的不确定性分析显示，预测校准良好，最大扰动ε=0.1时，ECE分数分别为0.0436（数据集1）和0.1159（数据集2）。此外，后验可解释AI分析表明，QuanvNeXt能有效识别并学习区分健康对照与抑郁患者的时频模式。总体而言，QuanvNeXt为基于脑电图的抑郁诊断提供了一种高效可靠的方法。
原文摘要 · Abstract (English)
This study presents QuanvNeXt, an end-to-end fully quanvolutional model for EEG-based depression diagnosis. QuanvNeXt incorporates a novel Cross Residual block, which reduces feature homogeneity and strengthens cross-feature relationships while retaining parameter efficiency. We evaluated QuanvNeXt on two open-source datasets, where it achieved an average accuracy of 93.1% and an average AUC-ROC of 97.2%, outperforming state-of-the-art baselines such as InceptionTime (91.7% accuracy, 95.9% AUC-ROC). An uncertainty analysis across Gaussian noise levels demonstrated well-calibrated predictions, with ECE scores remaining low (0.0436, Dataset 1) to moderate (0.1159, Dataset 2) even at the highest perturbation (ε = 0.1). Additionally, a post-hoc explainable AI analysis confirmed that QuanvNeXt effectively identifies and learns spectrotemporal patterns that distinguish between healthy controls and major depressive disorder. Overall, QuanvNeXt establishes an efficient and reliable approach for EEG-based depression diagnosis.
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。