Introduction: The mechanisms underlying cardiac arrhythmias in human hearts remain not fully elucidated. This study introduces an innovative experimental system that combines high-resolution optical mapping with torso-mimetic body surface potential mapping (BSPM) in explanted human hearts, aiming to correlate epicardial electrical activity with non-invasive signals directly.
Methods: An explanted heart from a transplant recipient was maintained in physiological condition using modified Langendorff perfusion. Mechanical uncoupling was achieved via blebbistatin perfusion, allowing simultaneous optical mapping (JPW-6003 dye, 500 fps acquisition) and electrical recording through a custom hexagonal tank with 30 torso-mimicking electrodes (4 kHz sampling). Analysis was performed in controlled pacing sequences and during recording of a spontaneous tachycardia. Activation patterns were analyzed using center-of-mass detection for electrical signals and 50% upstroke for optical data.
Results: The platform successfully maintained stable preparations for extended electrophysiological assessment. During pacing protocols, conduction patterns showed remarkable stability across all cycle lengths, demonstrating rate-independent propagation, highlighting the non-velocity adaptability of the heart. Spontaneous tachycardia revealed distinct lateralized activation propagating from anterior to posterior right ventricle in optical maps, while torso potentials showed centralized initiation patterns.
Conclusion: The dual-mapping platform for human hearts enables the study of clinical arrhythmias while preserving native tissue properties. By linking optical data with torso potential measurements, it contributes to improving non-invasive diagnostics and identifying complex patterns in failing hearts. This dissociation highlights the system's ability to uncover arrhythmia mechanisms and identify patterns in body surface potential propagation. These are preliminary findings, and further analyses are ongoing. Future efforts will increase electrode density and integrate machine learning for enhanced diagnostics and patient-specific arrhythmia management.