Electrocardiographic Imaging Without Imaging

Electrocardiographic Imaging (ECGI) is a noninvasive technique that reveals the propagation of potentials on the heart surface. In the standard approach, imaging such as CT or MRI is needed to obtain a torso/heart geometry, limiting possibilities for large-scale cost-effective deployment. An approach was developed to estimate heart surface potentials solely from body surface potentials and a torso geometry, which can be easily obtained with e.g. a camera. This approach relies on projecting both the epicardial and torso potentials onto a rectangular image for each time instant. For the torso, this consists of a projection onto a cylinder, which is then opened up into a rectangular image. For the ventricular epicardium, it consists of a projection onto a bullseye plot, followed by an unwrapping to a rectangular image based on the polar coordinates of the bullseye plot. This approach allows for maintaining a consistent location of potentials in the two different plots. A deep learning architecture based on a Pix2Pix network is then used to perform an image-to-image translation for all time instants concurrently. In essence this is a sequential conditional Generative Adversarial Network (scGAN). The network was trained on 8 healthy subjects as well as 22 idiopathic ventricular fibrillation (IVF) patients and tested on 3 healthy subjects and 7 IVF patients. When compared with reconstructions from standard ECGI, this approach achieved an average Mean Absolute Error (MAE) for the heart surface potential maps (HSPMs) of 0.012 ± 0.011, and an average similarity index measure (SSIM) of 0.984 ± 0.026. For the electrograms (EGMs), the average MAE obtained was 0.004 ± 0.004, and the average Pearson Correlation Coefficient (PCC) 0.643 ± 0.352. The absolute mean time differences between estimated and reference activation and recovery times were 6.048 ± 5.188 ms and 18.768 ± 17.299 ms respectively. These results demonstrate a performance comparable to standard ECGI without the need for CT/MRI. This approach could allow for the deployment of ECGI for first screening or patient follow-up, where standard ECGI is not feasible.