Sparse-to-Dense Body Surface Potential Mapping using A Structural Similarity-Enhanced Attention GAN

Body surface potential (BSP) mapping (BSPM) from reduced lead sparse electrocardiogram (ECG) signals is of high clinical relevance. Cost-efficient and non-invasive cardiac activity localization of myocardial infarction scars and arrhythmic sources are a few of its potential applications. However, most BSP reconstruction algorithms’ stability suffers from the underlying ill-posed inverse problem and poor morphological fidelity. Hence, model-constrained regularization is generally employed to incorporate physiological knowledge about the spatio-temporal BSP dynamics. In this treatise, we leverage the recent generative adversarial networks (GAN) paradigm and propose a multi-head attention-based pix2pix GAN architecture with an integrated structural similarity metric for high-fidelity BSPM from sparse ECG sensing. The attention mechanism ensures morphological saliency, while the structural similarity-based loss function regularizes the ill-posed reconstruction during model training and preserves the reconstructed BSP’s intricate fiducial morphology, which is critical for subsequent cardiac activity localization. The proposed BSPM framework has been tested on measured and synthetically generated BSP data utilizing a forward electrophysiology pipeline. The morphologically preserved BSP generated from the proposed model can potentially lead to improved, cost-efficient, and non-invasive cardiac activity monitoring.