Effects of heart position on the body-surface electrocardiogram

MacLeod, Robert S.; Ni, Quan; Punske, Bonnie B.; Ershler, Philip R.; Yılmaz, Bülent; Taccardi, Bruno

doi:10.1054/jelc.2000.20357

Cited by 47 publications

(45 citation statements)

References 10 publications

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“…The influence of geometrical factors on ECG parameters has been extensively investigated in the past [5][6][7]19], but the influence of geometry on QRS morphology and VCG parameters has not been studied before in patients with wide QRS complexes. Our results, based on an in-silico approach using patient-specific geometries, demonstrate that morphological features of the ECG (in particular notching/slurring and ID-time) and voltage-dependent VCG parameters (QRS amplitude, QRS area, T-wave amplitude, and T-wave area) are severely affected by geometry modifications, influencing the diagnosis of LBBB.…”

Section: Discussionmentioning

confidence: 99%

“…The heart was shifted with 1-cm steps up to 6 cm to the left and to the right along the x-axis and up and down along the z-axis as these shift magnitudes were used in previous studies [5,12]. The heart was rotated with 5°steps up to 30°around the y-axis (anteroposterior) to a more horizontal or vertical orientation.…”

Section: Changes In Heart Position and Orientationmentioning

confidence: 99%

“…The ECG morphology is sensitive to geometrical factors such as heart-torso geometry, body position, respiration, and body habitus [5,6]. Moreover, ventricular enlargement, as noted in heart-failure (HF) patients, may rotate the heart around the anteroposterior axis to a more horizontal orientation [7].…”

Section: Introductionmentioning

confidence: 99%

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An in-silico analysis of the effect of heart position and orientation on the ECG morphology and vectorcardiogram parameters in patients with heart failure and intraventricular conduction defects

Nguyên¹,

Potse²,

Regoli³

et al. 2015

Journal of Electrocardiology

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Aim: The aim of this study was to investigate the influence of geometrical factors on the ECG morphology and vectorcardiogram (VCG) parameters. Methods: Patient-tailored models based on five heart-failure patients with intraventricular conduction defects (IVCDs) were created. The heart was shifted up to 6 cm to the left, right, up, and down and rotated ±30°around the anteroposterior axis. Precordial electrodes were shifted 3 cm down.Results: Geometry modifications strongly altered ECG notching/slurring and intrinsicoid deflection time. Maximum VCG parameter changes were small for QRS duration (− 6% to + 10%) and QRS-T angle (− 6% to + 3%), but considerable for QRS amplitude (− 36% to + 59%), QRS area (− 37% to + 42%), T-wave amplitude (− 41% to + 36%), and T-wave area (− 42% to + 33%). Conclusion:The position of the heart with respect to the electrodes is an important factor determining notching/slurring and voltage-dependent parameters and therefore must be considered for accurate diagnosis of IVCDs.

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Section: Discussionmentioning

confidence: 99%

Section: Changes In Heart Position and Orientationmentioning

confidence: 99%

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An in-silico analysis of the effect of heart position and orientation on the ECG morphology and vectorcardiogram parameters in patients with heart failure and intraventricular conduction defects

Nguyên¹,

Potse²,

Regoli³

et al. 2015

Journal of Electrocardiology

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“…6 This raises the question: how much detail is required to produce a sufficiently accurate forward model? Previous studies have shown that it is necessary to include realistic heart and torso geometries, [7][8][9][10] but the extent to which the inhomogeneous electric properties of internal structures needs to be accounted for is less clear. There is a reasonable consensus that body surface potential distributions are not significantly affected by the liver, stomach, 11 blood vessels, 11,12 intestines, spleen, kidney, spine, sternum, and other bones.…”

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confidence: 99%

Forward Problem of Electrocardiography

Bear

Cheng

LeGrice

et al. 2015

Circ: Arrhythmia and Electrophysiology

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Background-The relationship between epicardial and body surface potentials defines the forward problem of electrocardiography. A robust formulation of the forward problem is instrumental to solving the inverse problem, in which epicardial potentials are computed from known body surface potentials. Here, the accuracy of different forward models has been evaluated experimentally. Methods and Results-Body surface and epicardial potentials were recorded simultaneously in anesthetized closed-chest pigs (n=5) during sinus rhythm, and epicardial and endocardial ventricular pacing (65 records in total). Body surface potentials were simulated from epicardial recordings using experiment-specific volume conductor models constructed from magnetic resonance imaging. Results for homogeneous (isotropic electric properties) and inhomogeneous (incorporating lungs, anisotropic skeletal muscle, and subcutaneous fat) forward models were compared with measured body surface potentials. Correlation coefficients were 0.85±0.08 across all animals and activation sequences with no significant difference between homogeneous and inhomogeneous solutions (P=0.85). Despite this, there was considerable variance between simulated and measured body surface potential distributions. Differences between the body surface potential extrema predicted with homogeneous forward models were 55% to 78% greater than observed (P<0.05) and attenuation of potentials adjacent to extrema were 10% to 171% greater (P<0.03). The length and orientation of the vector between potential extrema were also significantly different. Inclusion of inhomogeneous electric properties in the forward model reduced, but did not eliminate these differences. Conclusions-These results demonstrate that homogeneous volume conductor models introduce substantial spatial inaccuracies in forward problem solutions. This probably affects the precision of inverse reconstructions of cardiac potentials, in which this assumption is made. (Circ Arrhythm Electrophysiol. 2015;8:677-684.

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“…Some of these studies focused on the effects of variations in the position of the heart for different individuals on body surface potentials [15,28,34]. Other studies showed the negative effects of geometric errors on inverse problem solutions [30,16,40,35,13,5,19,2].…”

Section: Introductionmentioning

confidence: 99%

A Kalman filter-based approach to reduce the effects of geometric errors and the measurement noise in the inverse ECG problem

Aydın

Doğrusöz

2011

Med Biol Eng Comput

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In this article, we aimed to reduce the effects of geometric errors and measurement noise on the inverse problem of Electrocardiography (ECG) solutions. We used the Kalman filter to solve the inverse problem in terms of epicardial potential distributions. The geometric errors were introduced into the problem via wrong determination of the size and location of the heart in simulations. An error model, which is called the enhanced error model (EEM), was modified to be used in inverse problem of ECG to compensate for the geometric errors. In this model, the geometric errors are modeled as additive Gaussian noise and their noise variance is added to the measurement noise variance. The Kalman filter method includes a process noise component, whose variance should also be estimated along with the measurement noise. To estimate these two noise variances, two different algorithms were used: (1) an algorithm based on residuals, (2) expectation maximization algorithm. The results showed that it is important to use the correct noise variances to obtain accurate results. The geometric errors, if ignored in the inverse solution procedure, yielded incorrect epicardial potential distributions. However, even with a noise model as simple as the EEM, the solutions could be significantly improved.

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Effects of heart position on the body-surface electrocardiogram

Cited by 47 publications

References 10 publications

An in-silico analysis of the effect of heart position and orientation on the ECG morphology and vectorcardiogram parameters in patients with heart failure and intraventricular conduction defects

An in-silico analysis of the effect of heart position and orientation on the ECG morphology and vectorcardiogram parameters in patients with heart failure and intraventricular conduction defects

Forward Problem of Electrocardiography

A Kalman filter-based approach to reduce the effects of geometric errors and the measurement noise in the inverse ECG problem

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