A compact matrix model for atrial electrograms for tissue conductivity estimation

Finding the hidden parameters of the cardiac electrophysiological model would help to gain more insight on the mechanisms underlying atrial fibrillation, and subsequently, facilitate the diagnosis and treatment of the disease in later stages. In this work, we aim to estimate tissue conductivity from recorded electrograms as an indication of tissue (mal)functioning. To do so, we first develop a simple but effective forward model to replace the computationally intensive reaction-diffusion equations governing the electrical propagation in tissue. Using the simplified model, we present a compact matrix model for electrograms based on conductivity. Subsequently, we exploit the simplicity of the compact model to solve the ill-posed inverse problem of estimating tissue conductivity. The algorithm is demonstrated on simulated data as well as on clinically recorded data. The results show that the model allows to efficiently estimate the conductivity map. In addition, based on the estimated conductivity, realistic electrograms can be regenerated demonstrating the validity of the model. •In this study we developed a compact matrix model for atrial electrograms to show its linear dependence on the conductivity vector, enabling the estimation of this parameter from the recorded electrograms.•Using the forward model, we formulated an inverse problem for conductivity estimation using sparse and low rank matrix regularization.•We performed the approach on simulated and clinically recorded data. The results show that despite the low resolution and all simplifying assumptions, the model can efficiently estimate the conductivity map and regenerate realistic electrograms.•The results show that the proposed approach outperforms the two provided reference methods specialy in case of anisotropy and inhomogeneity in the tissue.