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

Area of Science:

• Cardiology
• Psychology
• Biomedical Engineering

Background:

• Established correlation between mental and cardiac health.
• Need for noninvasive methods to study emotional impact on the heart.
• Limitations in existing electrocardiographic imaging (ECGI) models with diverse datasets.

Purpose of the Study:

• To explore how emotions influence cardiac health.
• To develop an enhanced ECGI model capable of reconstructing cardiac electrical activity across various emotional states.
• To investigate the correlation and variability of cardiac activity under stress.

Main Methods:

• Utilized body surface potential data and the ECGI model.
• Proposed Emotional Potential Conversion CycleGAN for information fusion and data alignment.
• Validated the model using WESAD and SWELL datasets.
• Conducted correlation and variability studies of cardiac activity under stress.

Main Results:

• Achieved 91.92% accuracy in emotion recognition.
• Demonstrated successful data conversion while preserving emotional integrity.
• Identified stress-induced cardiac electrical activity fluctuations concentrated around the atrioventricular node and Purkinje fibers.
• Indicated a generalized association among heart regions during stress.

Conclusions:

• The Emotional Potential Conversion CycleGAN is a practical approach for studying emotional states and cardiac health.
• Findings suggest a potential link between stress, cardiac electrical activity, and possible pre-excitation syndrome.
• The study provides valuable insights for future research on the mind-heart connection.