What is the clinical evidence from this study?

Study design: Other. Population: Normal cardiac electrophysiology (n=3). Intervention: Physiology-based regularization (PBR) vs. Traditional Tikhonov regularization. Primary outcome: Correlation coefficient between recorded and reconstructed epicardial electrograms (p=<0.05).

November 21, 2016Open Access

Physiology-based regularization of the electrocardiographic inverse problem

Q: What are the key findings of this study?

Physiology-based regularization recovered details of heart-surface electrograms lost with traditional Tikhonov regularization, attaining higher correlation coefficients and improved estimation of recovery times.

Q: What does this research mean for the field?

Physiology-based regularization recovers details of heart-surface electrograms lost with traditional Tikhonov regularization, attaining higher correlation coefficients and improved estimation of recovery times. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

MCMatthijs CluitmansMaastricht University MCMichael ClerxUniversity of Nottingham NVNele VandersickelGhent University Hospital

Key Result

Physiology-based regularization recovered details of heart-surface electrograms lost with traditional Tikhonov regularization, attaining higher correlation coefficients and improved estimation of recovery times.

Structured PICO

Does physiology-based regularization improve the reconstruction of heart-surface electrograms from body-surface recordings compared to traditional Tikhonov regularization in dog models?

Population

In vivo dog models

Intervention

Physiology-based regularization (PBR) for the electrocardiographic inverse problem

Comparator

Traditional Tikhonov regularization

Outcome

Accuracy of reconstructed heart-surface electrograms (correlation coefficients and estimation of recovery times)surrogate

A novel physiology-based regularization method improves the noninvasive reconstruction of cardiac electrical activity from body-surface ECGs in a preclinical model.

Main Result

p-value: p=<0.05

Limitations

The basis is created from cellular action potential simulations, whereas the actual inverse reconstruction is in terms of local electrograms.
Limited by the healthy status of the animals, preventing investigation of the effect of local tissue abnormalities like myocardial infarction.
No information about tissue anisotropy was available, so it was not included in the simulations.

Abstract

The inverse problem of electrocardiography aims at noninvasively reconstructing electrical activity of the heart from recorded body-surface electrocardiograms. A crucial step is regularization, which deals with ill-posedness of the problem by imposing constraints on the possible solutions. We developed a regularization method that includes electrophysiological input. Body-surface potentials are recorded and a computed tomography scan is performed to obtain the torso-heart geometry. Propagating waveforms originating from several positions at the heart are simulated and used to generate a set of basis vectors representing spatial distributions of potentials on the heart surface. The real heart-surface potentials are then reconstructed from the recorded body-surface potentials by finding a sparse representation in terms of this basis. This method, which we named 'physiology-based regularization' (PBR), was compared to traditional Tikhonov regularization and validated using in vivo recordings in dogs. PBR recovered details of heart-surface electrograms that were lost with traditional regularization, attained higher correlation coefficients and led to improved estimation of recovery times. The best results were obtained by including approximate knowledge about the beat origin in the PBR basis.

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