ECGI with a Deep Neural Network and 2D Normalized Body Surface Potential Maps

Electrocardiographic Imaging (ECGI) reconstructs heart surface potentials (HSPs) from body surface potentials (BSPs) using a patient-specific torso-heart geometry derived from CT or MRI. Potential inaccuracies in the estimate of the torso-heart geometry, and of the electrode positions on the body surface may limit the accuracy of the reconstructed HSPs. In this study, we aim at providing a proof-of-principle Deep Neural Network (DNN) which directly maps BSPs to HSPs, without the need to estimate a transfer matrix for the forward problem. In particular, we propose a torso normalization method that can turn the original torso geometry into a 2D image, and transform it into a normalized body surface potential map (nBSPM). In this paper, we did this for four dog geometries. A 4-layer back propagation (BP) neural network is built and trained with the nBSPMs as the input, and the pixel values of corresponding heart surface potential maps (HSPMs) as the output. The experiments show that the mean-squared error (MSE) on the validation data set decreases and converges to around 3.0. The value of the Pearson correlation coefficient between the original and reconstructed HSPMs on the test data set is larger than 0.92. This indicates that the proposed model is suitable for reconstructing HSPs from BSPs.