The Inverse Problem of Electrocardiography

Pullan, Andrew J.; Cheng, Leo K.; Nash, Martyn P.; Ghodrati, Alireza; MacLeod, Rob S.; Brooks, Dana H.

doi:10.1007/978-0-85729-871-3_9

Cited by 5 publications

(9 citation statements)

References 132 publications

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“…It is well-known that this problem is ill-posed [23], and thus the matrix A is ill-conditioned with a smoothly decaying singular spectrum. Consequently, estimates of X are highly sensitive to both noise in the measured Y and errors in the forward model A .…”

Section: Background: Forward and Inverse Problemsmentioning

confidence: 99%

Using Transmural Regularization and Dynamic Modeling for Noninvasive Cardiac Potential Imaging of Endocardial Pacing With Imprecise Thoracic Geometry

Erem

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Orellana

et al. 2014

IEEE Trans. Med. Imaging

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Cardiac electrical imaging from body surface potential measurements is increasingly being seen as a technology with the potential for use in the clinic, for example for pre-procedure planning or during-treatment guidance for ventricular arrhythmia ablation procedures. However several important impediments to widespread adoption of this technology remain to be effectively overcome. Here we address two of these impediments: the difficulty of reconstructing electric potentials on the inner (endocardial) as well as outer (epicardial) surfaces of the ventricles, and the need for full anatomical imaging of the subject’s thorax to build an accurate subject-specific geometry. We introduce two new features in our reconstruction algorithm: a non-linear low-order dynamic parameterization derived from the measured body surface signals, and a technique to jointly regularize both surfaces. With these methodological innovations in combination, it is possible to reconstruct endocardial activation from clinically acquired measurements with an imprecise thorax geometry. In particular we test the method using body surface potentials acquired from three subjects during clinical procedures where the subjects’ hearts were paced on their endocardia using a catheter device. Our geometric models were constructed using a set of CT scans limited in axial extent to the immediate region near the heart. The catheter system provides a reference location to which we compare our results. We compare our estimates of pacing site localization, in terms of both accuracy and stability, to those reported in a recent clinical publication [1], where a full set of CT scans were available and only epicardial potentials were reconstructed.

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Section: Background: Forward and Inverse Problemsmentioning

confidence: 99%

Using Transmural Regularization and Dynamic Modeling for Noninvasive Cardiac Potential Imaging of Endocardial Pacing With Imprecise Thoracic Geometry

Erem

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Orellana

et al. 2014

IEEE Trans. Med. Imaging

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“…ECGI requires body-surface electrocardiogram recordings, usually with high spatial coverage and density, referred to as body surface potential mapping (BSPM), together with a geometric model, typically derived from medical images [12], and a computational implementation of a numerical solution to an ill-posed inverse problem [3]. Perhaps because of the diverse and specialized knowledge and experience required to pursue research in ECGI, progress has typically been tied to collaborative research among mathematicians, physicists, biomedical engineers, computer scientists, and cardiologists.…”

Section: Introductionmentioning

confidence: 99%

The Consortium on Electrocardiographic Imaging

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Dhamala

Potyagaylo

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Electrocardiographic imaging (ECGI) has recently gained attention as a viable diagnostic tool for reconstructing cardiac electrical activity in normal hearts as well as in cardiac arrhythmias. However, progress has been limited by the lack of both standards and unbiased comparisons of approaches and techniques across the community, as well as the consequent difficulty of effective collaboration across research groups.. To address these limitations, we created the Consortium for Electrocardiographic Imaging (CEI), with the objective of facilitating collaboration across the research community in ECGI and creating standards for comparisons and reproducibility.Here we introduce CEI and describe its two main efforts, the creation of EDGAR, a public data repository, and the organization of three collaborative workgroups that address key components and applications in ECGI. Both EDGAR and the workgroups will facilitate the sharing of ideas, data and methods across the ECGI community and thus address the current lack of reproducibility, broad collaboration, and unbiased comparisons.

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“…The inverse problem, by contrast, is to estimate a useful characterization of cardiac sources given the same geometric description and the measured body surface (or probe) potentials [1], [2]. …”

Section: Introductionmentioning

confidence: 99%

A toolkit for forward/inverse problems in electrocardiography within the SCIRun problem solving environment

Burton

Tate

Erem

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Computational modeling in electrocardiography often requires the examination of cardiac forward and inverse problems in order to non-invasively analyze physiological events that are otherwise inaccessible or unethical to explore. The study of these models can be performed in the open-source SCIRun problem solving environment developed at the Center for Integrative Biomedical Computing (CIBC). A new toolkit within SCIRun provides researchers with essential frameworks for constructing and manipulating electrocardiographic forward and inverse models in a highly efficient and interactive way. The toolkit contains sample networks, tutorials and documentation which direct users through SCIRun-specific approaches in the assembly and execution of these specific problems.

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The Inverse Problem of Electrocardiography

Cited by 5 publications

References 132 publications

Using Transmural Regularization and Dynamic Modeling for Noninvasive Cardiac Potential Imaging of Endocardial Pacing With Imprecise Thoracic Geometry

Using Transmural Regularization and Dynamic Modeling for Noninvasive Cardiac Potential Imaging of Endocardial Pacing With Imprecise Thoracic Geometry

The Consortium on Electrocardiographic Imaging

A toolkit for forward/inverse problems in electrocardiography within the SCIRun problem solving environment

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