Effects of a priori parameter selection in minimum relative entropy method on inverse electrocardiography problem

2018-01-01
ONAK, Onder Nazim
Serinağaoğlu Doğrusöz, Yeşim
WEBER, GERHARD WİLHELM
Show full item record
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
144
views
0
downloads
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 decomposition and statistical approaches such as Bayesian Maximum A Posteriori estimation and Kalman filter have been applied. Statistical methods have yielded accurate inverse solutions; however, they require knowledge of a good a priori probability density function, or state transition definition. Minimum relative entropy (MRE) is an approach for inferring probability density function from a set of constraints and prior information, and may be an alternative to those statistical methods since it operates with more simple prior information definitions. However, success of the MRE method also depends on good choice of prior parameters in the form of upper and lower bound values, expected uncertainty in the model and the prior mean. In this paper, we explore the effects of each of these parameters on the solution of inverse ECG problem and discuss the limitations of the method. Our results show that the prior expected value is the most influential of the three MRE parameters.
Inverse Problems, Inverse Electrocardiography, Minimum Relative Entropy, Parameter Estimation, Regularization
https://hdl.handle.net/11511/42718
INVERSE PROBLEMS IN SCIENCE AND ENGINEERING
Department of Electrical and Electronics Engineering, Article

Suggestions

Improved performance of Bayesian solutions for inverse Electrocardiography using multiple information sources
Serinağaoğlu Doğrusöz, Yeşim; MACLEOD, Robert (2006-10-01)
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...
Genetic algorithm-based regularization parameter estimation for the inverse electrocardiography problem using multiple constraints
Serinağaoğlu Doğrusöz, Yeşim; Gavgani, Alireza Mazloumi (2013-04-01)
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...
Use of Activation Time Based Kalman Filtering in Inverse Problem of Electrocardiography
Aydin, Umit; Serinağaoğlu Doğrusöz, Yeşim (2008-11-27)
The goal of this study is to solve inverse problem of electrocardiography (ECG) in terms of epicardial potentials using body surface (torso) potential measurements. The problem is ill-posed and regularization must be applied. Kalman filter is one of the regularization approaches, which includes both spatial and temporal correlations of epicardial potentials. However, in order to use the Kalman filter, one needs the state transition matrix (STM) that models the time evolution of the epicardial potentials. In...
SOLVING THE INVERSE PROBLEM OF ELECTROCARDIOGRAPHY FOR SPONTANEOUS PVC LOCALIZATION: ANALYSIS OF CLINICAL ELECTROCARDIOGRAPHIC DATA
Rasoolzadeh, Nika; Serinağaoğlu Doğrusöz, Yeşim; Department of Scientific Computing (2023-1-27)
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...
Combination of conventional regularization methods and genetic algorithm for solving the inverse problem of electrocardiography
Sarikaya, Sedat; Weber, Gerhard-Wilhelm; Serinağaoğlu Doğrusöz, Yeşim (2010-07-16)
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 ...
Citation Formats
O. N. ONAK, Y. Serinağaoğlu Doğrusöz, and G. W. WEBER, “Effects of a priori parameter selection in minimum relative entropy method on inverse electrocardiography problem,” INVERSE PROBLEMS IN SCIENCE AND ENGINEERING, pp. 877–897, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42718.
METU IIS - Integrated Information Service open@metu.edu.tr