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)
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.
Feature-level consistency objective
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.
Encoder-agnostic framework
The proposed framework is encoder-agnostic and can be integrated with state space models and Transformer architectures.
Seizure detection experiments
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.
Combining structured corruption with representation alignment
These findings highlight the benefit of combining structured corruption with representation alignment for robust time-series learning.