The Inverse Problem of Electrocardiography

Pullan, Andrew J.; Cheng, Leo K.; Nash, Martyn P.; Brookes, Mike; Ghodrati, Alireza; MacLeod, RS

doi:10.1007/978-1-84882-046-3_9

Cited by 57 publications

(60 citation statements)

References 132 publications

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“…ECG imaging is an emerging technology that may provide cardiologists noninvasive insights into cardiac electrical activity [25,26,30,34]. ECG imaging uses multichannel ECG recordings from up to 252 channels.…”

Section: Introductionmentioning

confidence: 99%

Automatic camera-based identification and 3-D reconstruction of electrode positions in electrocardiographic imaging

Schulze¹,

Mackens²,

Potyagaylo³

et al. 2014

Biomedical Engineering / Biomedizinische Technik

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Electrocardiographic imaging (ECG imaging) is a method to depict electrophysiological processes in the heart. It is an emerging technology with the potential of making the therapy of cardiac arrhythmia less invasive, less expensive, and more precise. A major challenge for integrating the method into clinical workflow is the seamless and correct identification and localization of electrodes on the thorax and their assignment to recorded channels. This work proposes a camera-based system, which can localize all electrode positions at once and to an accuracy of approximately 1 ± 1 mm. A system for automatic identification of individual electrodes is implemented that overcomes the need of manual annotation. For this purpose, a system of markers is suggested, which facilitates a precise localization to subpixel accuracy and robust identification using an error-correcting code. The accuracy of the presented system in identifying and localizing electrodes is validated in a phantom study. Its overall capability is demonstrated in a clinical scenario.

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Section: Introductionmentioning

confidence: 99%

Automatic camera-based identification and 3-D reconstruction of electrode positions in electrocardiographic imaging

Schulze¹,

Mackens²,

Potyagaylo³

et al. 2014

Biomedical Engineering / Biomedizinische Technik

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“…The potential-based formulation of the forward/inverse problem of electrocardiography is based on the relation between potentials on a closed surface surrounding the heart, and the body surface [8]. The closed surface surrounding the heart is usually taken to be the epicardium.…”

Section: Inverse Reconstruction Of Epicardial Potentialsmentioning

confidence: 99%

Spatiotemporal Activation Time Estimation Improves Noninvasive Localization of Cardiac Electrical Activity

Cluitmans

Coll‐Font

Erem

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Electrocardiographic imaging (ECGI) reconstructs epicardial potentials and electrograms from body-surface electrocardiograms and a torso-heart geometry. For clinical purposes, local activation and recovery times are often more useful than epicardial electrograms. However, noise and fractionation can affect estimation of activation and recovery times from reconstructed electrograms. Here, we employ a method for activation and recovery time estimation that detects the simultaneous presence of spatial and temporal features associated with a passing wavefront and evaluate this in a series of canine experiments. We show that estimation of activation times is more accurate when this spatiotemporal approach is used, however, recovery times are best determined with a temporal-only approach. Additional spatial smoothing further benefits activation and recovery time estimation in all cases. This results in a median beat origin localization error of only one centimeter, which could expedite catheter-based diagnostic evaluation and ablation in clinical settings.

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“…Generally, the matrix that relates the heart surface potentials to the body surface potentials is not square and has a large condition number. Further, since there is often measurement noise in the body surface potential measurements, the inverse problem is ill-posed in the sense of Hadamard [1]. To overcome this ill-posedness, Tikhonov regularisation is used [34], but the drawback here is that a new, unknown, parameter (called the regularisation parameter) is introduced, thus making it difficult to find the heart surface potential distribution.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of electrocardiographic imaging using the method of fundamental solutions

Johnston

2018

Computers in Biology and Medicine

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Solving the inverse problem of electrocardiology via the Method of Fundamental Solutions has been proposed previously. The advantage of this approach is that it is a meshless method, so it is far easier to implement numerically than many other approaches. However, determining the heart surface potential distribution is still an ill-posed problem and thus requires some form of Tikhonov regularisation to obtain the required distributions. In this study, several methods for determining an "optimal" regularisation parameter are compared in the context of solving the inverse problem of electrocardiology via the Method of Fundamental Solutions. It is found that the Robust Generalised Cross-Validation method most often yields epicardial potential distributions with the least relative error when compared to the input distribution. The study also compares the inverse solutions obtained with the Method of Fundamental Solutions with those obtained in a previous study using the boundary element method. It is found that choosing the best solution methodology and regularisation parameter determination method depends on the particular scenario being considered.

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The Inverse Problem of Electrocardiography

Cited by 57 publications

References 132 publications

Automatic camera-based identification and 3-D reconstruction of electrode positions in electrocardiographic imaging

Automatic camera-based identification and 3-D reconstruction of electrode positions in electrocardiographic imaging

Spatiotemporal Activation Time Estimation Improves Noninvasive Localization of Cardiac Electrical Activity

Accuracy of electrocardiographic imaging using the method of fundamental solutions

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