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

Date

2019-05-01

Author

Onak, Onder Nazim
Serinağaoğlu Doğrusöz, Yeşim
Weber, Gerhard Wilhelm

Metadata

Show full item record

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

Item Usage Stats

245
views

0
downloads

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.

Subject Keywords

Biomedical Engineering, Computer Science Applications

URI

https://hdl.handle.net/11511/37365

Journal

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING

DOI

https://doi.org/10.1007/s11517-018-1934-9

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging Erenler, T; Serinağaoğlu Doğrusöz, Yeşim (Springer Science and Business Media LLC, 2019-10-01) In electrocardiographic imaging (ECGI), one solves the inverse problem of electrocardiography (ECG) to reconstruct equivalent cardiac sources based on the body surface potential measurements and a mathematical model of the torso. Due to attenuation and spatial smoothing within the torso, this inverse problem is ill-posed. Among many regularization approaches used in the ECG literature to overcome this ill-posedness, statistical techniques have received great attention because of their flexibility to represe...
Parallel implementation of the accelerated BEM approach for EMSI of the human brain ATASEVEN, YOLDAŞ; Akalin-Acar, Z.; Acar, C. E.; Gençer, Nevzat Güneri (Springer Science and Business Media LLC, 2008-07-01) Boundary element method (BEM) is one of the numerical methods which is commonly used to solve the forward problem (FP) of electro-magnetic source imaging with realistic head geometries. Application of BEM generates large systems of linear equations with dense matrices. Generation and solution of these matrix equations are time and memory consuming. This study presents a relatively cheap and effective solution for parallel implementation of the BEM to reduce the processing times to clinically acceptable valu...
A Kalman filter-based approach to reduce the effects of geometric errors and the measurement noise in the inverse ECG problem Aydin, Umit; Serinağaoğlu Doğrusöz, Yeşim (Springer Science and Business Media LLC, 2011-09-01) In this article, we aimed to reduce the effects of geometric errors and measurement noise on the inverse problem of Electrocardiography (ECG) solutions. We used the Kalman filter to solve the inverse problem in terms of epicardial potential distributions. The geometric errors were introduced into the problem via wrong determination of the size and location of the heart in simulations. An error model, which is called the enhanced error model (EEM), was modified to be used in inverse problem of ECG to compens...
Implementation and comparison of reconstruction algorithms for magnetic resonance-electric impedance tomography (mr-eit) Martin Lorca, Dario; Eyüboğlu, Behçet Murat; Department of Biomedical Engineering (2007) In magnetic resonance electrical impedance tomography (MR-EIT), crosssectional images of a conductivity distribution are reconstructed. When current is injected to a conductor, it generates a magnetic field, which can be measured by a magnetic resonance imaging (MRI) scanner. MR-EIT reconstruction algorithms can be grouped into two: current density based reconstruction algorithms (Type-I) and magnetic flux density based reconstruction algorithms (Type-II). The aim of this study is to implement a series of r...
The effects of geometric errors on inverse ECG solutions using Kalman filter and Bayesian MAP estimation Kalman filtre ve Bayes-MAP ile ters EKG çözümlerinde geometrik hatalarin etkisi Aydin, Ümit; Serinağaoğlu Doğrusöz, Yeşim (2009-10-27) Geometric errors in inverse ECG are usually the errors occur in the mathematical model used for solution due to wrong interpretation of heart's position and size, conductivities of organs in the model and electrode positions. In this study the effects of geometric errors in inverse ECG problem for Kalman filter and Bayes-AMP methods are studied Furthermore the method suggested by Kaipio et. al., which assumes that these geometric errors are additive noise and independent of the epicardial potentials, is imp...

Citation Formats

O. N. Onak, Y. Serinağaoğlu Doğrusöz, and G. W. Weber, “Evaluation of multivariate adaptive non-parametric reduced-order model for solving the inverse electrocardiography problem: a simulation study,” MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, pp. 967–993, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37365.