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Combination of conventional regularization methods and genetic algorithm for solving the inverse problem of electrocardiography
Date
2010-07-16
Author
Sarikaya, Sedat
Weber, Gerhard-Wilhelm
Serinağaoğlu Doğrusöz, Yeşim
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Distribution of electrical potentials over the surface of the heart, which is called the epicardial potential distribution, is a valuable tool to understand whether there is a defect in the heart. Direct measurement of these potentials requires highly invasive procedures. An alternative is to reconstruct these epicardial potentials non-invasively from the body surface potentials, which constitutes one form of the ill-posed inverse problem of electrocardiography (ECG). The goal of this study is to solve the inverse problem of ECG using several regularization methods and compare their performances. We employed Tikhonov Regularization, Truncated Singular Value Decomposition (TSVD), Least Squares QR (LSQR) methods in this study. We compared the effectiveness of these regularization methods to solve the ill-posed inverse ECG problem. Some of the regularization methods require a regularization parameter to solve the inverse problem. We used the well-known L-Curve method to obtain the regularization parameter. The performance of the regularization methods for solving the inverse ECG problem was also evaluated based on a realistic heart-torso model simulation protocol. In this paper, we also investigated the usage of genetic algorithm (GA) for regularizing the ill-posed inverse ECG problem. The results showed that GA can be applied to regularize the ill-posed problem when combined with the results of conventional regularization methods or additional information about solutions.
Subject Keywords
Inverse Problem
,
Regularization
,
Optimization
,
Regularization Parameter
,
Genetic Algorithm
,
Electrocardiography
URI
https://hdl.handle.net/11511/47717
DOI
https://doi.org/10.1109/hibit.2010.5478914
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
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Combination of conventional regularization methods and genetic algorithms for solving the inverse problem of electrocardiography
Sarıkaya, Sedat; Serinağaoğlu Doğrusöz, Yeşim; Department of Scientific Computing (2010)
Distribution of electrical potentials over the surface of the heart, i.e., the epicardial potentials, is a valuable tool to understand whether there is a defect in the heart. However, it is not easy to detect these potentials non-invasively. Instead, body surface potentials, which occur as a result of the electrical activity of the heart, are measured to diagnose heart defects. However the source electrical signals loose some critical details because of the attenuation and smoothing they encounter due to bo...
SOLVING THE INVERSE PROBLEM OF ELECTROCARDIOGRAPHY FOR SPONTANEOUS PVC LOCALIZATION: ANALYSIS OF CLINICAL ELECTROCARDIOGRAPHIC DATA
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The inverse problem of electrocardiography refers to the determination of the electrical activity of the heart from the body surface potential measurements (BSPM). Knowledge of the electrical activity state of the heart can provide valuable insights for the diagnosis of cardiac disorders and aid in the facilitation of the development of appropriate treatments. Consequently, efficient resolution of this problem has the potential to be of significant benefit to clinical practices, making it imperative to cont...
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In inverse electrocardiography, the goal is to estimate cardiac electrical sources from potential measurements on the body surface. It is by nature an ill-posed problem, and regularization must be employed to obtain reliable solutions. This paper employs the multiple constraint solution approach proposed in Brooks et al. (IEEE Trans Biomed Eng 46(1):3-18, 1999) and extends its practical applicability to include more than two constraints by finding appropriate values for the multiple regularization parameter...
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The goal in inverse electrocardiography (ECG) is to reconstruct cardiac electrical sources from body surface measurements and a mathematical model of torso-heart geometry that relates the sources to the measurements. This problem is ill-posed due to attenuation and smoothing that occur inside the thorax, and small errors in the measurements yield large reconstruction errors. To overcome this, ill-posedness, traditional regularization methods such as Tikhonov regularization and truncated singular value decom...
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The usual goal in inverse electrocardiography (ECG) is to reconstruct cardiac electrical sources from body surface potentials and a mathematical model that relates the sources to the measurements. Due to attenuation and smoothing that occurs in the thorax, the inverse ECG problem is ill-posed and imposition of a priori constraints is needed to combat this ill-posedness. When the problem is posed in terms of reconstructing heart surface potentials, solutions have not yet achieved clinical utility; limitation...
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S. Sarikaya, G.-W. Weber, and Y. Serinağaoğlu Doğrusöz, “Combination of conventional regularization methods and genetic algorithm for solving the inverse problem of electrocardiography,” 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47717.