Smart optogenetics for real-time automated control of cardiac electrical activity
S Deng, N Harlaar, J Zhang, S O Dekker, N N Kudryashova, H Zhou, C I Bart, T Jin, G Derevyanko, A V Panfilov, R Poelma, A A F De Vries, G Q Zhang, T De Coster, D A PijnappelsAbstract
Feedback control is fundamental to stabilizing dynamic systems, from machines to living tissues. In the heart, disturbances in electrical conduction can give rise to arrhythmias, with rhythms that may shift unpredictably. Existing treatments rely either on rapid but nonspecific shocks or on precisely targeted procedures that cannot adapt as the arrhythmia evolves. Ideally, anti-arrhythmic strategies should provide both a rapid response and adaptive precision.
To address this challenge, we present a platform integrating optical voltage mapping (OVM), machine learning (ML), and optogenetics for real-time detection and modulation of cardiac rhythm disorders in vitro. OVM enables high-resolution, spatiotemporal visualization of membrane potentials, allowing precise monitoring of cardiac rhythms. The ML module detects and localizes arrhythmic events and generates real-time control commands to a microLED array, specifying spatial and temporal light patterns that restore normal conduction. Finally, optogenetics provides light-based control of the electrical activity of excitable cells. Together these components enable autonomous real-time correction of abnormal cardiac rhythms.
This combination of ML-driven analysis and optogenetic control represents a significant advance in real-time electrophysiological interventions. Our hybrid approach efficiently restores normal rhythms in vitro, paving the way for future development of automated, closed-loop systems for treating bioelectrical disorders.