Accurate stress detection from physiological signals is often complicated by individual identity traits, which must first be identified before they can be effectively removed to improve model performance. To address this, we propose a method that combines Detrended Fluctuation Analysis (DFA) and Augmented Dickey-Fuller (ADF) Analysis to extract stable identity-related features from heart rate signals without relying on explicit labels. By masking these identity features and raw content features, we can effectively eliminate their impact on task-relevant stress signals using guided contrastive learning. Validated on the TILES-2018 and Firefighters datasets, our approach significantly improves stress detection accuracy, achieving F1 score gains of 5.3% and 8.1%, respectively, compared to baseline models. These results highlight the model’s enhanced ability to generalize to diverse populations while minimizing identity bias, ultimately improving the robustness and precision of stress detection systems.