You can find Supplementary Videos from our publications on our Youtube channel @cardiacvision or view or download them below.

Panoramic Voltage-Sensitive Optical Mapping of Contracting Hearts using Cooperative Multi-View Motion Tracking with 12 to 24 Cameras

We developed a novel panoramic optical mapping system with which it is possible to image action potential waves across the entire surface of contracting hearts. The Supplementary Videos 1-4 and interactive 3D renderings from our preprint are available here or on Youtube.

Deep Learning-based Prediction of Electrical Arrhythmia Circuits from Cardiac Motion: An In-Silico Study

In this study, we demonstrated in computer simulations that it is possible to compute electrical circuits, such as focal or reentrant waves, from ventricular deformation. The Supplementary Videos for our preprint are available here: Link

Reconstruction of Three-dimensional Scroll Waves in Excitable Media from Two-Dimensional Observations using Deep Neural Networks

In this study, we showed that deep convolutional neural networks can be used to reconstruct three-dimensional electrical wave dynamics simulated in bulk-shaped ventricular muscle tissue from two-dimensional observations on the surface of these tissues. The study may have practical relevance with regard to the estimation of transmural electrical activation patterns from superficial mesurements of the electrical activity on the heart's surface. The Supplementary Videos 1-5 can be found here.

Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models

In this study, we explored artificial intelligence-based generative modeling of cardiac excitation waves. We showed that diffusion models can be used to reconstruct and simulate electrical spiral and scroll wave dynamics. The Supplementary Videos for our preprint are available here: Link

Real-Time Optical Mapping of Contracting Cardiac Tissues With GPU-Accelerated Numerical Motion Tracking

In this study, we tested several motion tracking algorithms applied to optical mapping recordings showing deforming and fluorescing hearts and cell cultures. The study has led to the development of optimap, an open-source software package for the processing of fluorescence video data. Using this package, we demonstrated that numerical motion-stabilization of optical mapping recordings can be performed in real-time using GPU-acceleration. You can find the Supplementary Videos here: Link

Rotor Localization and Phase Mapping of Cardiac Excitation Waves Using Deep Neural Networks

In this study, we developed a deep learning-based technique for computing phase maps and phase singularities from electrical recordings of electrical impulse phenomena in cardiac tissue: Link