Electrogram-based Estimation of Myocardial Conduction Using Deep Neural Networks

Contact electrograms (EGMs) can be used to guide catheter ablation in the treatment of atrial fibrillation. However, our understanding of the link between electrophysiology
$(EP)$
and the underlying myocardial substrate is limited. We use neural networks and EGMs to estimate the amount of collagen within the field of view of the recording electrodes. EGMs were recorded from rat ventricular slices
$(n=15)$
, Samples were imaged using second harmonic generation
$(SHG)$
microscopy, allowing for quantification of collagen. A convolutional neural network (1D-ResNet) was trained to estimate collagen distribution from the recorded EGMs. Each electrogram, recorded for one cycle length, was paired with a collagen index for the corresponding electrode. The total number of samples was 91,239. We successfully estimated collagen index in the testing set, with an absolute error of
$0.022\pm 0.024$
, and a correlation coefficient of
$R=0.81$
between the predicted and true collagen amount. The network identifies main morphological features of the EGMs as useful features for quantifying collagen underneath the electrode. This work provides a framework and proof of concept that location of scar can be predicted from EGMS using neural networks.