Comparison and testing of least-squares time domain inverse solutions in electrocardiography |
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| Affiliation: | 2. Communications and Digital Signal Processing (CDSP) Center for Research and Graduate Studies, Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA;1. MIEMSS, Department of Surgery, University of Maryland, Baltimore, MD 21201, USA;1. Soft Computing Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai 81310, Malaysia;2. Neural Engineering Lab, Centre for Biomedical Engineering, Universiti Teknologi Malaysia, Malaysia;3. Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, Johor Malaysia, Malaysia;1. Laboratory of Cognitive Neuroscience, Center for Neuroprosthetics and Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), 9 Chemin des Mines, 1202, Geneva, Switzerland;2. Department of Neurology, University of Geneva, 24 Rue Micheli-du-Crest, 1211, Geneva, Switzerland;1. University of Southern California, United States;2. Ohio State University, United States;1. Institute of Physiology, Christian-Albrechts-Universität Kiel, Germany;2. International Institute for Theoretical Cardiology, Kiel, Germany;3. Institut für Pädagogik, Christian-Albrechts-Universität Kiel, Germany;4. Zentrum für konstruktive Erziehungswissenschaften, Christian-Albrechts-Universität Kiel, Germany |
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| Abstract: | The use of several mathematical methods for estimating epicardial ECG potentials from arrays of body surface potentials has been reported in the literature; most of these methods are based on least-squares reconstruction principles and operate in the time-space domain. In this paper we introduce a general Bayesian maximum a posteriori (MAP) framework for time domain inverse solutions in the presence of noise. The two most popular previously applied least-squares methods, constrained (regularized) least-squares and low-rank approximation through the singular value decomposition, are placed in this framework, each of them requiring the a priori knowledge of a ‘regularization parameter’, which defines the degree of smoothing to be applied to the inversion. Results of simulations using these two methods are presented; they compare the ability of each method to reconstruct epicardial potentials. We used the geometric configuration of the torso and internal organs of an individual subject as reconstructed from CT scans. The accuracy of each method at each epicardial location was tested as a function of measurement noise, the size and shape of the subarray of torso sensors, and the regularization parameter. We paid particular attention to an assessment of the potential of these methods for clinical use by testing the effect of using compact, small-size subarrays of torso potentials while maintaining a high degree of resolution on the epicardium. |
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