Enhanced PPG-based stress recognition: a transfer learning approach to internal vs. external stress

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Abstract Objective. To develop a comprehensive physiological dataset for assessing internal and external stress and to propose robust automated stress recognition methods based on photoplethysmographic (PPG) signals. Approach. We established the Internal and External Stress Dataset (IESD), comprising PPG signals from 107 participants subjected to four distinct stress-inducing paradigms. Exploratory analyses revealed significant differences in heart rate variability (HRV) across these paradigms, underscoring the necessity for advanced methods capable of differentiating various stress types. To address this, we introduced a transfer learning-based inter-paradigm stress recognition model utilizing a domain adversarial neural network combined with maximum mean discrepancy for robust feature extraction. Main results. Analysis identified significant differences between internal and external stress, as well as among different external paradigms. Our proposed model demonstrated superior accuracy in recognizing homologous stress compared to heterologous stress within the same target domain, achieving accuracies of 73.86% (TSST to ST) and 60.41% (TSST to VWT). Moreover, the deep feature extraction significantly improved recognition performance and robustness across both intra- and inter-paradigm contexts. Significance. This study provides a valuable dataset and advanced methodology to enhance automated stress detection capabilities, effectively differentiating internal and external stress. The application of deep learning significantly improves recognition accuracy, offering promising prospects for future research and practical applications in stress monitoring.

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