A Novel Approach to Explore Internal Cardiac Electrophysiological Pattern under Emotional Stress.

Abstract

Numerous psychological and clinical studies have confirmed a correlation between mental and cardiac health. We aim to explore this relationship further by examining how emotions influence cardiac health. By collecting body surface potential and utilizing the electrocardiographic imaging (ECGI) model, we can noninvasively and continuously reconstruct internal cardiac electrical activity. To enhance the existing ECGI model on various datasets, we propose an information fusion strategy called Emotional Potential Conversion CycleGAN. It enables data alignment across diverse datasets while preserving emotional information, allowing us to reconstruct cardiac electrical activity in various emotional states. Our results demonstrate successful data conversion while maintaining emotional integrity, achieving an impressive 91.92% accuracy in emotion recognition. We further validated this approach using publicly available datasets, WESAD and SWELL, which yielded consistent results. Additionally, we conducted preliminary investigations into the correlation and variability of cardiac activity across different sites under stress. The correlation study indicates a generalized association among various regions of the heart, while variability studies reveal that fluctuations in cardiac electrical activity during stress are primarily concentrated around the atrioventricular node and Purkinje fibers. This suggests a potential risk for pre-excitation syndrome, possibly due to the possible presence of a Kent bundle. Overall, we present a practical approach for studying the interplay between emotional states and cardiac health. Our findings indicate a potential relationship under stress that may provide valuable insights for future research.