Validation of a sequential data assimilation method applied to cardiac electrophysiology

Improved electroanatomical recordings and imaging capabilities prompts for trying to personalize cardiac electrical models. Though it is challenging, data assimilation is a family of methods relevant to this problem. It consists in estimating the state of the system thanks to observations. Among the two main approaches, variationnal and sequential, we choose to investigate the possibilities of a sequential method based on an atrial model and atrial electroanatomical maps. The method combines a state observer and a Kalman filter to estimate some parameters in the model.