Studies show that individual attributes such as age and gender significantly influence physiological responses and their correlation with stress, often forming complex and overlapping relationships. These attributes are essential for enhancing physiological signal-based stress detection. Our work leverages hypergraph in multi-attribute representation and tackles the challenge of redundant attributes that misguide embeddings. Our dynamic edge-selection mechanism for hypergraph-based metric learning (DESHM) enables the hypergraph to focus on selected stress-related attribute connections within groups. This batch-wise selection adapts to varying connections between batches and maximizes the effectiveness of metric learning. Evaluation of the TILES-2018 and Firefighter datasets shows promising results, further improving 3.99% in F1, 3.52% in BACC, and 25.27% in MCC compared to the best pairwise result on the TILES-2018 dataset. Our analysis shows that our mechanism prioritizes attributes that effectively represent stress levels, guiding hyper-graph clustering to achieve improved discriminability.