Use of Simulated Data for the Estimation of Prior Models in Kalman Filter-Based ECGI

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Dogrusoz Y., Erenler T.

Computing in Cardiology Conference (CinC), Rimini, Italy, 13 - 16 September 2020 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.22489/cinc.2020.329
  • City: Rimini
  • Country: Italy


Kalman filtering has been successfully applied to electrocardiographic imaging (ECGI) to improve the estimation accuracy, especially when a 'good' training set of epicardial potentials is available to estimate the prior statistics. Most methods in the literature use previously measured experimental data to obtain these training sets, which would not be feasible in a clinical application. In this study we explored the effectiveness of using simulated epicardial potentials and the corresponding BSPs for obtaining the prior models based on two approaches: maximum likelihood (MLIF) and maximum a posteriori (MAPIF) estimation. Our results showed that even using a simple simulation method, and large margin in the initial pacing location range (80mm here), simulated data can be used for defining the prior models in the Kalman filter-based-ECGI.