发表机构
University of Minnesota, Twin Cities（明尼苏达大学双城分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
DCRA通过将扩散前向过程作为结构化损坏调度器并引入特征级一致性目标，在噪声下对齐时间序列表示，提升EEG癫痫检测的鲁棒性和灵敏度。
AI 中文摘要
在噪声和分布偏移下学习时间序列信号的鲁棒表示仍然具有挑战性，尤其是在脑电图（EEG）和心电图（ECG）分析等临床应用中。我们提出了扩散条件表示对齐（DCRA），这是一种训练框架，将前向扩散过程重新用作表示学习的结构化损坏调度器。与依赖独立采样扰动的传统增强和基于一致性的方法不同，DCRA通过扩散前向过程引入结构化损坏轨迹，从而能够跨噪声水平实现连续且受控的表示演化。我们引入了一种特征级一致性目标，该目标在保留类别判别结构的同时，跨噪声水平对齐表示。这种机制促进了结构保持一致性，使得潜在空间中的特征轨迹平滑且语义连贯。所提出的框架与编码器无关，可与状态空间模型和Transformer架构集成。在CHB-MIT EEG数据集上的癫痫发作检测实验表明，DCRA在多种噪声条件下持续提高性能，并在低假阳性率下实现更高的灵敏度。分析表明，与基线和仅扩散模型相比，DCRA产生了更平衡和结构化的表示。这些发现强调了将结构化损坏与表示对齐相结合对鲁棒时间序列学习的益处。
英文摘要
Learning robust representations for time-series signals under noise and distribution shifts remains challenging, especially in clinical applications such as electroencephalogram (EEG) and electrocardiogram (ECG) analysis. We propose Diffusion-Conditioned Representation Alignment (DCRA), a training framework that repurposes the forward diffusion process as a structured corruption scheduler for representation learning. Different from conventional augmentation and consistency-based methods that rely on independently sampled perturbations, DCRA introduces a structured corruption trajectory via the diffusion forward process, which enables continuous and controlled representation evolution across noise levels. We introduce a feature-level consistency objective that aligns representations across noise levels while preserving class-discriminative structure. This mechanism promotes structure-preserving consistency, which enables smooth and semantically coherent feature trajectories in latent space. The proposed framework is encoder-agnostic and can be integrated with state space models and Transformer architectures. The seizure detection experiments on the CHB-MIT EEG dataset show that DCRA consistently improves performance under multiple noise conditions and achieves higher sensitivity at low false-positive rates. Analysis reveals that DCRA produces more balanced and structured representations compared to baseline and diffusion-only models. These findings highlight the benefit of combining structured corruption with representation alignment for robust time-series learning.
