Detection of Incomplete Left Bundle Branch Block by Noninvasive Electrocardiographic Imaging

Bear, Laura; Huntjens, Peter; Coronel, Ruben; Bernus, Olivier; Dallet, Corentin; Walton, Richard D.; Dubois, Rémi

doi:10.22489/cinc.2016.113-200

Cited by 2 publications

(2 citation statements)

References 8 publications

(10 reference statements)

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“…Ex-vivo data were collected from 6 Langendorffperfused pig hearts with standard Tyrode's solution in a human-shaped torso tank. A flexible 108-electrode sock was placed over the epicardium and a pacing lead on the right ventricle (RV) apex [10]. Tank and sock potentials were acquired at 128 electrodes at 2 kHz (BioSemi, the Netherlands), referenced to a Wilson's central terminal.…”

Section: Bspms From Ex-vivo Experimentsmentioning

confidence: 99%

Non-Invasive Assessment of Spatiotemporal Organization of Ventricular Fibrillation Through Principal Component Analysis

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Potse

Puyo

et al. 2017

Computing in Cardiology Conference (CinC)

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Introduction Ventricular fibrillation (VF) is the main cause of sudden cardiac death, but we lack tools to predict the evolution of its complexity. This study proposes novel VF complexity markers obtained by principal component analysis (PCA) of body surface potential maps (BSPMs). Methods BSPMs were divided in 0.5-s segments, each projected on the 3D PCA subspace determined in the previous frame. Reconstruction error was expressed in terms of norm d and angle cosine cos(α), and the nondipolar component index (NDI) quantified the energy of the first 3 PCA eigenvalues. These markers quantified changes in complexity between the beginning and the end of 24 VF episodes and 5 control sinus rhythm (SR) recordings. They were also tested on 6 BSPMs from a torso-tank model during and after ex-vivo pig heart perfusion. Differences between in-silico BSPM right and left leads were then verified in 4 human VF simulations, with a reentrant source in the right ventricle. Results Higher NDI and d and lower cos(α) denoted higher complexity at the end of VF (p < 0.0001). No changes occurred in SR (p > 0.05). The indices also underlined higher disorganization in exvivo non-perfused VF (p < 0.0001). A similar trend was observed in the right in-silico BSPM leads (p < 0.05). Conclusions PCA can non-invasively quantify changes in VF complexity.

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Section: Bspms From Ex-vivo Experimentsmentioning

confidence: 99%

Non-Invasive Assessment of Spatiotemporal Organization of Ventricular Fibrillation Through Principal Component Analysis

Meo

Potse

Puyo

et al. 2017

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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Detection of Incomplete Left Bundle Branch Block by Noninvasive Electrocardiographic Imaging

Abstract: VEU (p=0.06), or TAT values (p=0.21).

Cited by 2 publications

References 8 publications

Non-Invasive Assessment of Spatiotemporal Organization of Ventricular Fibrillation Through Principal Component Analysis

Non-Invasive Assessment of Spatiotemporal Organization of Ventricular Fibrillation Through Principal Component Analysis

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

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