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Evaluation of multivariate adaptive non-parametric reduced-order model for solving the inverse electrocardiography problem: a simulation study

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Abstract

In the inverse electrocardiography (ECG) problem, the goal is to reconstruct the heart’s electrical activity from multichannel body surface potentials and a mathematical model of the torso. Over the years, researchers have employed various approaches to solve this ill-posed problem including regularization, optimization, and statistical estimation. It is still a topic of interest especially for researchers and clinicians whose goal is to adopt this technique in clinical applications. Among the wide range of mathematical tools available in the fields of operational research, inverse problems, optimization, and parameter estimation, spline-based techniques have been applied to inverse problems in several areas. If proper spline bases are chosen, the complexity of the problem can be significantly reduced while increasing estimation accuracy. However, there are few studies within the context of the inverse ECG problem that take advantage of this property of the spline-based approaches. In this paper, we evaluate the performance of Multivariate Adaptive Regression Splines (MARS)-based method for the solution of the inverse ECG problem using two different collections of simulated data. The results show that the MARS-based method improves the inverse ECG solutions and is “robust” to modeling errors, especially in terms of localizing the arrhythmia sources.

Multivariate adaptive non-parametric model for inverse ECG problem.

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Acknowledgments

The authors would like to thank Dr. Robert S. MacLeod from University of Utah, Nora Eccles Harrison Cardiovascular Research and Training Institute for the data used in this study. The qualitative assessments in this work became possible by software, Map3d, which was supported by the National Institute of General Medical Sciences of the National Institutes of Health under grant number P41 GM103545-18. The authors also would like to thank Karlsruhe Institute of Technology (KIT) and Walther H. W. Schulze and his colleagues for the data used in this study, and the Consortium of ECGI for facilitating data sharing for all researchers.

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  1. Institute of Applied Mathematics, Middle East Technical University, Üniversiteler Mahallesi Dumlupınar Bulvarı, No:1, 06800, Çankaya/Ankara, Turkey

    Önder Nazım Onak, Yesim Serinagaoglu Dogrusoz & Gerhard Wilhelm Weber

  2. Department of Electrical and Electronics Engineering, Middle East Technical University, Üniversiteler Mahallesi Dumlupınar Bulvarı, No:1, 06800, Çankaya/Ankara, Turkey

    Yesim Serinagaoglu Dogrusoz

  3. Faculty of Engineering Management, Poznan University of Technology, Ul. Strzelecka 11, 60-965, Poznan, Poland

    Gerhard Wilhelm Weber

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Onak, Ö.N., Dogrusoz, Y.S. & Weber, G.W. Evaluation of multivariate adaptive non-parametric reduced-order model for solving the inverse electrocardiography problem: a simulation study. Med Biol Eng Comput 57, 967–993 (2019). https://doi.org/10.1007/s11517-018-1934-9

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