A MARS-Based Approach for PVC Localization Using Clinical Data Klinik Veriler Kullanilarak PVC Lokalizasyonu i in MARS Yakla simi

Date

2025-01-01

Author

Almus, Canberk
Serinağaoğlu Doğrusöz, Yeşim
Svehlikova, Jana
Ondrusova, Beata

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Electrocardiographic Imaging (ECGI) is a noninvasive technique for identifying premature ventricular contraction (PVC) origins, which aids radiofrequency ablation (RFA). This study evaluates a framework based on Multivariate Adaptive Regression Splines (MARS) across different geometric models, including homogeneous, inhomogeneous, and hybrid models, using clinical data. Patient-specific heart surface potentials were simulated, and corresponding body surface potentials (BSPs) were computed using the boundary element method (BEM). These BSPs, along with the heart surface potentials, were used to train the regression model. Activation times were computed, and the earliest activation point was identified as the estimated origin. The performance was evaluated using localization error (LE), and compared with Tikhonov regularization. The best-performing geometric model demonstrated better or comparable performance compared to Tikhonov regularization.

Subject Keywords

clinical data, Electrocardiographic Imaging, inverse problem, MARS, PVC

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105015471807&origin=inward
https://hdl.handle.net/11511/115935

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Department of Electrical and Electronics Engineering, Conference / Seminar