Simultaneous High-Resolution Electrical Imaging of Endocardial, Epicardial and Torso-Tank Surfaces Under Varying Cardiac Metabolic Load and Coronary Flow

Shome, Shibaji; MacLeod, Rob S.

doi:10.1007/978-3-540-72907-5_33

Cited by 11 publications

(21 citation statements)

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“…One as yet unexplored source of error in these studies is insufficient cardiac source representation, i.e., either inadequate coverage or spatial density of coverage of the cardiac sources. For example, many experimental validation studies use an epicardial sock electrode array to record cardiac surface potentials from the animal heart (Ramsey et al, 1977 ; Stanley et al, 1986 ; Shome and MacLeod, 2007 ; Bear et al, 2015 ). A common limitation of these epicardial socks is that they position electrodes on the ventricles only, ignoring the atria.…”

Section: Introductionmentioning

confidence: 99%

Reducing Error in ECG Forward Simulations With Improved Source Sampling

et al. 2018

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A continuing challenge in validating electrocardiographic imaging (ECGI) is the persistent error in the associated forward problem observed in experimental studies. One possible cause of this error is insufficient representation of the cardiac sources; cardiac source measurements often sample only the ventricular epicardium, ignoring the endocardium and the atria. We hypothesize that measurements that completely cover the pericardial surface are required for accurate forward solutions. In this study, we used simulated and measured cardiac potentials to test the effect of different levels of spatial source sampling on the forward simulation. Not surprisingly, increasing the source sampling over the atria reduced the average error of the forward simulations, but some sampling strategies were more effective than others. Uniform and random distributions of samples across the atrial surface were the most efficient strategies in terms of lowest error with the fewest sampling locations, whereas “single direction” strategies, i.e., adding to the atrioventricular (AV) plane or atrial roof only, were the least efficient. Complete sampling of the atria is needed to eliminate errors from missing cardiac sources, but while high density sampling that covers the entire atria yields the best results, adding as few as 11 electrodes on the atria can significantly reduce these errors. Future validation studies of the ECG forward simulations should use a cardiac source sampling that takes these considerations into account, which will, in turn, improve validation and understanding of ECGI.

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Section: Introductionmentioning

confidence: 99%

Reducing Error in ECG Forward Simulations With Improved Source Sampling

et al. 2018

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“…Technical validation encompasses a broad range of cardiac source models, forward model formulations, inverse methods, and pre- and post-processing techniques. These studies are typically executed using either analytical ( Rudy et al, 1979 ) or simulated potentials ( Dubois et al, 2016 ; Figuera et al, 2016 ; Svehlikova et al, 2018 ), ex vivo torso tank experiments ( Oster et al, 1997 ; Shome and Macleod, 2007 ; Bear et al, 2018a ) and in vivo animal studies ( Liu et al, 2012 ; Oosterhoff et al, 2016 ; Cluitmans et al, 2017 ; Bear et al, 2018b ), with more limited results from humans ( Ghanem et al, 2005 ; Sapp et al, 2012 ; Erem et al, 2014 ; Schulze, 2015 ; Punshchykova et al, 2016 ). For simplicity, here we have organized technical validation according to the different features that may be extracted from ECGI, irrespective of source models.…”

Section: Technical Validationmentioning

confidence: 99%

“…Electrograms are one of the most common features reconstructed with ECGI, as they provide useful information to clinicians, directly relatable to invasive recordings. Ground truth data is available through simulations ( Simms and Geselowitz, 1995 ; Wang et al, 2010 ; Figuera et al, 2016 ; Janssen et al, 2017 ), recordings obtained with epicardial, endocardial and transmural electrode arrays (with upwards of 200 electrodes) in ex vivo ( Oster et al, 1997 ; Shome and Macleod, 2007 ; Bear et al, 2018b ) and in vivo ( Zhang et al, 2005 ; Han et al, 2011 ; Liu et al, 2012 ; Oosterhoff et al, 2016 ; Cluitmans et al, 2017 ; Bear et al, 2018b ) experimental models, and invasive mapping clinically ( Ghanem et al, 2005 ; Sapp et al, 2012 ; Punshchykova et al, 2016 ). Most validation studies to date use a global evaluation of the QRS, T, or QRS-T waveform reconstruction using correlation and/or error metrics, to demonstrate the accuracy in the overall topology and/or amplitude of electrograms (example in Figure 3 ).…”

Section: Technical Validationmentioning

confidence: 99%

“…Over the past four decades, ECGI has seen considerable development, from purely analytical studies ( Rudy et al, 1979 ; Figuera et al, 2016 ; Svehlikova et al, 2018 ), to torso tank ( Oster et al, 1997 , 1998 ; Ramanathan and Rudy, 2001 ; Shome and Macleod, 2007 ; Bear et al, 2018a ) and large animal models ( Liu et al, 2012 ; Oosterhoff et al, 2016 ; Cluitmans et al, 2017 ; Bear et al, 2018b ) and finally application in humans ( Ghanem et al, 2005 ; Horáček et al, 2011 ; Haissaguerre et al, 2014 ; Schulze, 2015 ; Punshchykova et al, 2016 ). It is now increasingly used for academic research and in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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Validation and Opportunities of Electrocardiographic Imaging: From Technical Achievements to Clinical Applications

et al. 2018

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Electrocardiographic imaging (ECGI) reconstructs the electrical activity of the heart from a dense array of body-surface electrocardiograms and a patient-specific heart-torso geometry. Depending on how it is formulated, ECGI allows the reconstruction of the activation and recovery sequence of the heart, the origin of premature beats or tachycardia, the anchors/hotspots of re-entrant arrhythmias and other electrophysiological quantities of interest. Importantly, these quantities are directly and non-invasively reconstructed in a digitized model of the patient’s three-dimensional heart, which has led to clinical interest in ECGI’s ability to personalize diagnosis and guide therapy. Despite considerable development over the last decades, validation of ECGI is challenging. Firstly, results depend considerably on implementation choices, which are necessary to deal with ECGI’s ill-posed character. Secondly, it is challenging to obtain (invasive) ground truth data of high quality. In this review, we discuss the current status of ECGI validation as well as the major challenges remaining for complete adoption of ECGI in clinical practice. Specifically, showing clinical benefit is essential for the adoption of ECGI. Such benefit may lie in patient outcome improvement, workflow improvement, or cost reduction. Future studies should focus on these aspects to achieve broad adoption of ECGI, but only after the technical challenges have been solved for that specific application/pathology. We propose ‘best’ practices for technical validation and highlight collaborative efforts recently organized in this field. Continued interaction between engineers, basic scientists, and physicians remains essential to find a hybrid between technical achievements, pathological mechanisms insights, and clinical benefit, to evolve this powerful technique toward a useful role in clinical practice.

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“…These socks allow for relatively high spatial sampling and coverage of the ventricular epicardium, yet exclude sampling of the atrial epicardium [2, 3]. Recordings from this region of the heart would include possible atrial or ventricular endocardial sources from the forward simulation; excluding them can introduce substantial error in the forward simulation.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Source Sampling to Reduce Error in ECG Forward Simulations

Tate

Gillette

Burton

et al. 2017

Computing in Cardiology Conference (CinC)

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A continuing challenge in validating ECG Imaging is the persistent error in the associated forward problem observed in experimental studies. One possible cause of error is insufficient representation of the cardiac sources, which is often measured from only the ventricular epicardium, ignoring the endocardium and the atria. We hypothesize that measurements that completely cover the heart are required for accurate forward solutions. In this study, we used simulated and measured cardiac potentials to test the effect of different levels of sampling on the forward simulation. We found that omitting source samples on the atria increases the peak RMS error by a mean of 464 μV when compared the the fully sampled cardiac surface. Increasing the sampling on the atria in stages reduced the average error of the forward simulation proportionally to the number of additional samples and revealed some strategies may reduce error with fewer samples, such as adding samples to the AV plane and the atrial roof. Based on these results, we can design a sampling strategy to use in future validation studies.

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Simultaneous High-Resolution Electrical Imaging of Endocardial, Epicardial and Torso-Tank Surfaces Under Varying Cardiac Metabolic Load and Coronary Flow

Cited by 11 publications

References 9 publications

Reducing Error in ECG Forward Simulations With Improved Source Sampling

Reducing Error in ECG Forward Simulations With Improved Source Sampling

Validation and Opportunities of Electrocardiographic Imaging: From Technical Achievements to Clinical Applications

Analyzing Source Sampling to Reduce Error in ECG Forward Simulations

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