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Effects of a priori parameter selection in minimum relative entropy method on inverse electrocardiography problem
Date
2018-01-01
Author
ONAK, Onder Nazim
Serinağaoğlu Doğrusöz, Yeşim
WEBER, GERHARD WİLHELM
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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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 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.
Subject Keywords
Inverse Problems
,
Inverse Electrocardiography
,
Minimum Relative Entropy
,
Parameter Estimation
,
Regularization
URI
https://hdl.handle.net/11511/42718
Journal
INVERSE PROBLEMS IN SCIENCE AND ENGINEERING
DOI
https://doi.org/10.1080/17415977.2017.1369979
Collections
Department of Electrical and Electronics Engineering, Article