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Session: The heart
Session starts: Thursday 24 January, 10:30
Presentation starts: 11:00
Room: Lecture room 559

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Bahareh Abdi (Circuits and systems, TU Delft)
Richard C. Hendriks (Circuits and systems, TU Delft)
Alle-Jan van der Veen (Circuits and systems, TU Delft)
Natasja M.S. de Groot (Department of Cardiology, Erasmus University Medical Center)

Abstract:
Gaining more understanding on the mechanism underlying atrial fibrillation depends partly on electrophysiological models. However, these models are typically rather complex, hampering further improvements. Signal processing based modelling and analysis can be a great help to bring such models to a higher abstraction level and easier estimate important underlying model parameters and, subsequently, better facilitate the diagnosis and treatment in a later stage. 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. This parsimonious model opens up new possibilities for further processing of electrograms. Using the simplified model, we present a linear measurement model for electrograms based on conductivity. Subsequently, we exploit the simplicity of the linear model to solve the ill-posed inverse problem of estimating tissue conductivity. As model validation, we present an initial algorithm for tissue conductivity estimation. 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.