Assessment of regularization techniques for electrocardiographic imaging

Abstract A widely used approach to solving the inverse problem in electrocardiography involves computing potentials on the epicardium from measured electrocardiograms (ECGs) on the torso surface. The main challenge of solving this electrocardiographic imaging (ECGI) problem lies in its intrinsic ill...

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Bibliographic Details
Published in:Journal of electrocardiology 2014-01, Vol.47 (1), p.20-28
Main Authors: Milanič, Matija, PhD, Jazbinšek, Vojko, PhD, MacLeod, Robert S., PhD, Brooks, Dana H., PhD, Hren, Rok, PhD
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Body Surface Potential Mapping - methods
Cardiovascular
Computer Simulation
Conjugate gradient
Data Interpretation, Statistical
Diagnosis, Computer-Assisted - methods
Electrocardiographic inverse problem
Heart Conduction System - physiology
Heart Rate - physiology
Humans
MINRES method
Models, Cardiovascular
Reproducibility of Results
Sensitivity and Specificity
Tikhonov regularization
Total variation
Truncated singular value decomposition
ν-method
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Summary:Abstract A widely used approach to solving the inverse problem in electrocardiography involves computing potentials on the epicardium from measured electrocardiograms (ECGs) on the torso surface. The main challenge of solving this electrocardiographic imaging (ECGI) problem lies in its intrinsic ill-posedness. While many regularization techniques have been developed to control wild oscillations of the solution, the choice of proper regularization methods for obtaining clinically acceptable solutions is still a subject of ongoing research. However there has been little rigorous comparison across methods proposed by different groups. This study systematically compared various regularization techniques for solving the ECGI problem under a unified simulation framework, consisting of both 1) progressively more complex idealized source models (from single dipole to triplet of dipoles), and 2) an electrolytic human torso tank containing a live canine heart, with the cardiac source being modeled by potentials measured on a cylindrical cage placed around the heart. We tested 13 different regularization techniques to solve the inverse problem of recovering epicardial potentials, and found that non-quadratic methods (total variation algorithms) and first-order and second-order Tikhonov regularizations outperformed other methodologies and resulted in similar average reconstruction errors.
ISSN:0022-0736
1532-8430
DOI:10.1016/j.jelectrocard.2013.10.004