Reconstruction of Atrial Ectopic Focal and Re:entrant Excitations from Body Surface Potentials: Insights from 3D Virtual Human Atria and Torso

Alday, Erick Andres Perez; Colman, Michael A.; Zhang, Henggui

doi:10.22489/cinc.2016.205-397

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…The boundary element method (BEM) was used to compute the electric potentials on the surface of the body (Fig 1C), resulting from an applied current density, J i , obtained from the electrical activity of the ventricular tissue-models. Details can be found in previous studies [35,36]. Once the electric potential ϕ is known, the magnetic field, B, was obtained by discretizing the volume into m homogenous elements and using a BEM of the Biot-Savart law [37,38]:…”

Section: Description Of Mathematical Modelsmentioning

confidence: 99%

Comparison of electric- and magnetic- cardiograms produced by myocardial ischemia in models of the human ventricle and torso

Alday

Zhang

Colman

et al. 2015

2015 Computing in Cardiology Conference (CinC)

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Myocardial ventricular ischemia arises from a lack of blood supply to the heart, which may cause abnormal repolarization and excitation wave conduction patterns in the tissue, leading to cardiac arrhythmias and even sudden death. Current diagnosis of cardiac ischemia by the 12-lead electrocardiogram (ECG) has limitations as they are insensitive in many cases and may show unnoticeable differences to normal patterns. As the magnetic field provides extra information on cardiac excitation and is more sensitive to tangential currents to the surface of the chest, whereas the electric field is more sensitive to flux currents, it has been hypothesized that the magnetocardiogram (MCG) may provide a complementary method to the ECG in ischemic diagnosis. However, it is unclear yet about the differences in sensitivity regions of body surface ECG and MCG signals to ischemic conditions. The aim of this study was to investigate such differences by using 12-, 36-ECG and 36-MCG computed from multi-scale biophysically detailed computational models of the human ventricles and torso in both control and ischemic conditions. It was shown that ischemia produced changes in the ECG and MCG signals in the QRS complex, T-wave and ST-segment, with greater relative differences seen in the 36-lead ECG and MCG as compared to the 12-leads ECG (34% and 37% vs 26%, respectively). The 36-lead ECG showed more averaged sensitivity than the MCG in the change of T-wave due to ischemia (37% vs 32%, respectively), whereas the MCG showed greater sensitivity than the ECG in the change of the ST-segment (50% vs 40%, respectively). In addition, both MCG and ECG showed regional-dependent changes to ischemia, but with MCG showing a stronger correlation between ischemic region in the heart. In conclusion, MCG shows more sensitivity than ECG in response to ischemia, which may provide an alternative method for the diagnosis of ischemia.

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Section: Description Of Mathematical Modelsmentioning

confidence: 99%

Comparison of electric- and magnetic- cardiograms produced by myocardial ischemia in models of the human ventricle and torso

Alday

Zhang

Colman

et al. 2015

2015 Computing in Cardiology Conference (CinC)

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“…Electrocardiographic imaging (ECGi) is a novel, painless and (relatively) economic method to map the electrical activation and repolarization patterns of the heart (Ghosh et al, 2011; Alday et al, 2016; Bear et al, 2016; Perez Alday et al, 2016; Zhang et al, 2016), and presents the possibility to better understand cardiac excitation patterns and provide a priori information to guide invasive surgical procedures, improving success rates and reducing procedure time (Silva et al, 2009; Dubois et al, 2015; Zhang et al, 2016). Based on solving the inverse problem of electrocardiography, with the heart acting as an electrical source inside the volume conductor of the body, ECGi aims to reconstruct the electrical activity on the surface of the heart using body surface potential (BSP) maps obtained from torso surface multi-array electrocardiogram (ECG) systems (Macfarlane et al, 2010; Rudy, 2013; Perez-Alday et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

Effects of Heart Rate and Ventricular Wall Thickness on Non-invasive Mapping: An in silico Study

et al. 2019

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Background: Non-invasive cardiac mapping—also known as Electrocardiographic imaging (ECGi)—is a novel, painless and relatively economic method to map the electrical activation and repolarization patterns of the heart, providing a valuable tool for early identification and diagnosis of conduction abnormalities and arrhythmias. Moreover, the ability to obtain information on cardiac electrical activity non-invasively using ECGi provides the potential for a priori information to guide invasive surgical procedures, improving success rates, and reducing procedure time. Previous studies have shown the influence of clinical variables, such as heart rate, heart size, endocardial wall, and body composition on surface electrocardiogram (ECG) measurements. The influence of clinical variables on the ECG variability has provided information on cardiovascular control and its abnormalities in various pathologies. However, the effects of such clinical variables on the Body Surface Potential (BSP) and ECGi maps have yet to be systematically investigated. Methods: In this study we investigated the effects of heart size, intracardiac thickness, and heart rate on BSP and ECGi maps using a previously-developed 3D electrophysiologically-detailed ventricles-torso model. The inverse solution was solved using the three different Tikhonov regularization methods. Results: Through comparison of multiple measures of error/accuracy on the ECGi reconstructions, our results showed that using different heart geometries to solve the forward and inverse problems produced a larger estimated focal excitation location. An increase of ~2 mm in the Euclidean distance error was observed for an increase in the heart size. However, the estimation of the location of focal activity was still able to be obtained. Similarly, a Euclidean distance increase was observed when the order of regularization was reduced. For the case of activation maps reconstructed at the same ectopic focus location but different heart rates, an increase in the errors and Euclidean distance was observed when the heart rate was increased. Conclusions: Non-invasive cardiac mapping can still provide useful information about cardiac activation patterns for the cases when a different geometry is used for the inverse problem compared to the one used for the forward solution; rapid pacing rates can induce order-dependent errors in the accuracy of reconstruction.

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Reconstruction of Atrial Ectopic Focal and Re:entrant Excitations from Body Surface Potentials: Insights from 3D Virtual Human Atria and Torso

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Cited by 2 publications

References 15 publications

Comparison of electric- and magnetic- cardiograms produced by myocardial ischemia in models of the human ventricle and torso

Comparison of electric- and magnetic- cardiograms produced by myocardial ischemia in models of the human ventricle and torso

Effects of Heart Rate and Ventricular Wall Thickness on Non-invasive Mapping: An in silico Study

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