25First evidence that differences in the t-wave upslope of the body surface ECG reflect right to left dispersion of repolarization in the intact human heart

Summary Objectives: In this paper, we present a unified electrodynamic heart model that permits simulations of the body surface potentials generated by the heart in motion. The inclusion of motion in the heart model significantly improves the accuracy of the simulated body surface potentials and therefore also the 12-lead ECG. Methods: The key step is to construct an electromechanical heart model. The cardiac excitation propagation is simulated by an electrical heart model, and the resulting cardiac active forces are used to calculate the ventricular wall motion based on a mechanical model. The source-field point relative position changes during heart systole and diastole. These can be obtained, and then used to calculate body surface ECG based on the electrical heart-torso model. Results: An electromechanical biventricular heart model is constructed and a standard 12-lead ECG is simulated. Compared with a simulated ECG based on the static electrical heart model, the simulated ECG based on the dynamic heart model is more accordant with a clinically recorded ECG, especially for the ST segment and T wave of a V1-V6 lead ECG. For slight-degree myocardial ischemia ECG simulation, the ST segment and T wave changes can be observed from the simulated ECG based on a dynamic heart model, while the ST segment and T wave of simulated ECG based on a static heart model is almost unchanged when compared with a normal ECG. Conclusions: This study confirms the importance of the mechanical factor in the ECG simulation. The dynamic heart model could provide more accurate ECG simulation, especially for myocardial ischemia or infarction simulation, since the main ECG changes occur at the ST segment and T wave, which correspond with cardiac systole and diastole phases.

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Multiresolution wavelet analysis of the body surface ECG before and after angioplasty

Annals of Biomedical Engineering ◽

10.1007/bf02584455 ◽

1995 ◽

Vol 23 (5) ◽

pp. 553-561 ◽

Cited By ~ 14

Author(s):

Boris Gramatikov ◽

Sun Yi-chun ◽

Herve Rix ◽

Pere Caminal ◽

Nitish V. Thakor

Keyword(s):

Wavelet Analysis ◽

Body Surface ◽

The Body ◽

Surface Ecg ◽

Before And After ◽

Multiresolution Wavelet Analysis ◽

Multiresolution Wavelet

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Coherence between body surface ECG leads and intracardiac signals increases during the first 10 s of ventricular fibrillation in the human heart

Physiological Measurement ◽

10.1088/0967-3334/20/2/305 ◽

1999 ◽

Vol 20 (2) ◽

pp. 159-166 ◽

Cited By ~ 3

Author(s):

Richard H Clayton ◽

Alan Murray

Keyword(s):

Ventricular Fibrillation ◽

Body Surface ◽

Human Heart ◽

Surface Ecg ◽

Ecg Leads

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A Fully-Coupled Electro-Mechanical Whole-Heart Computational Model: Influence of Cardiac Contraction on the ECG

Frontiers in Physiology ◽

10.3389/fphys.2021.778872 ◽

2021 ◽

Vol 12 ◽

Author(s):

Robin Moss ◽

Eike Moritz Wülfers ◽

Steffen Schuler ◽

Axel Loewe ◽

Gunnar Seemann

Keyword(s):

Body Surface ◽

In Silico ◽

Computational Cost ◽

Cardiac Motion ◽

The Body ◽

Cardiac Contraction ◽

P Wave ◽

Qrs Complex ◽

T Wave ◽

Whole Heart

The ECG is one of the most commonly used non-invasive tools to gain insights into the electrical functioning of the heart. It has been crucial as a foundation in the creation and validation of in silico models describing the underlying electrophysiological processes. However, so far, the contraction of the heart and its influences on the ECG have mainly been overlooked in in silico models. As the heart contracts and moves, so do the electrical sources within the heart responsible for the signal on the body surface, thus potentially altering the ECG. To illuminate these aspects, we developed a human 4-chamber electro-mechanically coupled whole heart in silico model and embedded it within a torso model. Our model faithfully reproduces measured 12-lead ECG traces, circulatory characteristics, as well as physiological ventricular rotation and atrioventricular valve plane displacement. We compare our dynamic model to three non-deforming ones in terms of standard clinically used ECG leads (Einthoven and Wilson) and body surface potential maps (BSPM). The non-deforming models consider the heart at its ventricular end-diastatic, end-diastolic and end-systolic states. The standard leads show negligible differences during P-Wave and QRS-Complex, yet during T-Wave the leads closest to the heart show prominent differences in amplitude. When looking at the BSPM, there are no notable differences during the P-Wave, but effects of cardiac motion can be observed already during the QRS-Complex, increasing further during the T-Wave. We conclude that for the modeling of activation (P-Wave/QRS-Complex), the associated effort of simulating a complete electro-mechanical approach is not worth the computational cost. But when looking at ventricular repolarization (T-Wave) in standard leads as well as BSPM, there are areas where the signal can be influenced by cardiac motion of the heart to an extent that should not be ignored.

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Wavelet analysis and time-frequency distributions of the body surface ECG before and after angioplasty

Computer Methods and Programs in Biomedicine ◽

10.1016/s0169-2607(00)00060-2 ◽

2000 ◽

Vol 62 (2) ◽

pp. 87-98 ◽

Cited By ~ 36

Author(s):

Boris Gramatikov ◽

Jeffrey Brinker ◽

Sun Yi-chun ◽

Nitish V. Thakor

Keyword(s):

Wavelet Analysis ◽

Body Surface ◽

The Body ◽

Frequency Distributions ◽

Time Frequency ◽

Surface Ecg ◽

Before And After

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The repolarization pattern of ARVC patients, healthy gene carriers and controls as analyzed with a 252-leads Body Surface Mapping Vest

European Heart Journal ◽

10.1093/ehjci/ehaa946.0337 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

V Kommata ◽

M.I Elshafie ◽

M Perez ◽

R Augustine ◽

C Blomstrom-Lundquist

Keyword(s):

Body Surface ◽

Funding Source ◽

Surface Mapping ◽

Pattern Type ◽

T Wave ◽

Gene Carriers ◽

Surface Ecg ◽

Back Panel ◽

The Right

Abstract Background Repolarization abnormalities have a central role on the diagnosis of ARVC according to recent HRS consensus document from 2019 stating that T wave inversion in the right precordial leads is a major criteria if it appears in V1-V3 or a minor criteria if it appears in only V1-V2. Purpose The aim of our study was to investigate whether repolarization patterns as recorded by a Body Surface Mapping (BSM) system consisting of a vest with 252 ECG leads, could differentiate ARVC patients and even gene carriers from normal individuals. Our hypothesis is that the method can potentially identify repolarization disturbances earlier or better than conventional 12-lead ECG. Method 12 definite ARVC patients, 20 healthy gene carriers and 8 family members who tested negative for the family mutation (controls) were included. All patients underwent 12-lead ECG, including right precordial leads (V4R) and BSM recordings. Repolarization (T-wave polarity and concordance with QRS complex vector) was analyzed qualitatively in all BSM recordings, the results of which were displayed on a color code map (fig.1). Results The mean age was 49.6, 43.6 and 38.8 years in ARVC patients, healthy gene carriers and controls, respectively. The number of males in the three groups were 8/12, 8/20 and 5/8, respectively. All 8 controls had similar repolarization patterns with negative and concordant T waves on the right back panel, and T waves that successively changed from negative concordant (green) to positive disconcordant (red) and finally positive concordant (blue) on the left front panel (pattern 1). All 12 ARVC patients had different repolarization patterns as compared to the controls. Two of these patients had no apparent repolarization changes on conventional 12 lead ECG. The pattern type 2 repolarization, as defined by same pattern as the controls at the right back panel but different pattern at the front left panel was seen in 3/12 ARVC patients. The remaining 9 ARVC patients had different repolarization patterns both on the front and on the back panel (pattern 3). Among gene carriers, 15 had a normal repolarization pattern (pattern 1) and 5 demonstrated an abnormal repolarization pattern (4 had pattern type 2 and one pattern 3) despite normal surface ECG. Conclusions Using BSM recordings, abnormal repolarization patterns can be detected in all ARVC patients, even in those without repolarization changes on conventional surface ECG. The observation that 25% of gene carriers had divergent repolarization patterns, may indicate an early stage of the disease, and be used as an early diagnostic marker of the disease. Further and larger studies are warranted to confirm these observations. Repolarisation patterns recorded by BSM Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Selanders Stiftelse

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