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

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2020-01-01

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Serinağaoğlu Doğrusöz, Yeşim

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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.

URI

https://hdl.handle.net/11511/94171

DOI

https://doi.org/10.22489/cinc.2020.329

Conference Name

Computing in Cardiology Conference (CinC)

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Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

Y. Serinağaoğlu Doğrusöz, “Use of Simulated Data for the Estimation of Prior Models in Kalman Filter-Based ECGI,” presented at the Computing in Cardiology Conference (CinC), Rimini, İtalya, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/94171.