Learning robust representations for physiological time-series signals continues to pose a substantial challenge in developing efficient few-shot learning applications. This difficulty is largely due to the complex pathological variations in biosignals. In this context, this paper introduces a manifold-aware Diffusion-Augmented Contrastive Learning (DACL) framework, which efficiently leverages the generative structure of latent diffusion models with the discriminative power of supervised contrastive learning. The proposed framework operates within a contextualized scattering latent space derived from Scattering Transformer (ST) features. Within a contrastive learning framework, we employ a forward diffusion process in the scattering latent space as a structured manifold-aware feature augmentation technique. We assessed the proposed framework using the PhysioNet 2017 ECG benchmark dataset. The proposed method achieved a competitive AUROC of 0.9741 in the task of detecting atrial fibrillation from a single-lead ECG signal. The proposed framework achieved performance on par with relevant state-of-the-art related works. In-depth evaluation findings suggest that early-stage diffusion serves as an ideal "local manifold explorer," producing embeddings with greater precision than typical augmentation methods while preserving inference efficiency.