“Body surface” potentials produced by the eccentric dipole in the heart wall of the nonhomogeneous volume conductor

Abstract Aim To assess the effect of inhomogeneities in the conductivity of different tissues, such as blood and lung tissue, on the body surface potentials generated by atrial electrical activity. Methods A 64-lead ECG from a healthy subject was recorded. The subject's geometries of torso, lungs, heart, and blood cavities were derived by magnetic resonance imaging. These geometries were used to construct a numerical volume conductor model. The boundary element method was applied to simulate the potentials on the surface of the thorax generated by the atria. The equivalent double layer served as the source description during depolarization. Recorded body surface potentials were used as a check on the simulations. Subsequently, the conductivities in the model were varied to determine their influence on P wave morphology and amplitude. Results The model with realistic conductivity values for blood and lungs produced potentials that closely matched the measured ones (correlation 98%). The subsequent variation of conductivity of blood and lungs revealed a major influence on P wave morphology and amplitude: a mean reduction in amplitude by 42%, with pronounced inter-lead differences. Conclusion The inhomogeneities of lungs and atrial blood cavities need to be incorporated in volume conductor models linking atrial electric activity to body surface potentials.

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A comparison of simulated QRS isointegral maps resulting from pacing at adjacent sites: Implications for the spatial resolution of pace mapping using body surface potentials*1

Journal of Cardiothoracic and Vascular Anesthesia ◽

10.1016/s1053-0770(98)90307-x ◽

1998 ◽

Vol 31 ◽

pp. 135-144

Author(s):

R HREN

Keyword(s):

Spatial Resolution ◽

Body Surface ◽

Surface Potentials ◽

Pace Mapping

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The Role of Computer Modeling in Electrocardiography

Methods of Information in Medicine ◽

10.1055/s-0038-1634809 ◽

1990 ◽

Vol 29 (04) ◽

pp. 282-288 ◽

Cited By ~ 2

Author(s):

A. van Oosterom

Keyword(s):

Electrical Activity ◽

Computer Modeling ◽

Body Surface ◽

Electrical Current ◽

The Body ◽

Volume Conductor ◽

Current Generator ◽

Cardiac Electrical Activity ◽

Source Models

AbstractThis paper introduces some levels at which the computer has been incorporated in the research into the basis of electrocardiography. The emphasis lies on the modeling of the heart as an electrical current generator and of the properties of the body as a volume conductor, both playing a major role in the shaping of the electrocardiographic waveforms recorded at the body surface. It is claimed that the Forward-Problem of electrocardiography is no longer a problem. Several source models of cardiac electrical activity are considered, one of which can be directly interpreted in terms of the underlying electrophysiology (the depolarization sequence of the ventricles). The importance of using tailored rather than textbook geometry in inverse procedures is stressed.

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Computing the Depolarization Sequence at the Ventricular Surface from Body Surface Potentials

Pediatric and Fundamental Electrocardiography - Developments in Cardiovascular Medicine ◽

10.1007/978-1-4613-2323-5_5 ◽

1987 ◽

pp. 75-89 ◽

Cited By ~ 10

Author(s):

Adriaan van Oosterom

Keyword(s):

Body Surface ◽

Surface Potentials ◽

Ventricular Surface

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Cardiac Activation Maps Reconstruction: A Comparative Study Between Data-Driven and Physics-Based Methods

Frontiers in Physiology ◽

10.3389/fphys.2021.686136 ◽

2021 ◽

Vol 12 ◽

Author(s):

Amel Karoui ◽

Mostafa Bendahmane ◽

Nejib Zemzemi

Keyword(s):

Neural Networks ◽

Comparative Study ◽

Body Surface ◽

Data Driven ◽

Diagnostic Tools ◽

L1 Norm ◽

Surface Potentials ◽

Body Surface Potential ◽

Data Driven Approach ◽

Classic Technique

One of the essential diagnostic tools of cardiac arrhythmia is activation mapping. Noninvasive current mapping procedures include electrocardiographic imaging. It allows reconstructing heart surface potentials from measured body surface potentials. Then, activation maps are generated using the heart surface potentials. Recently, a study suggests to deploy artificial neural networks to estimate activation maps directly from body surface potential measurements. Here we carry out a comparative study between the data-driven approach DirectMap and noninvasive classic technique based on reconstructed heart surface potentials using both Finite element method combined with L1-norm regularization (FEM-L1) and the spatial adaptation of Time-delay neural networks (SATDNN-AT). In this work, we assess the performance of the three approaches using a synthetic single paced-rhythm dataset generated on the atria surface. The results show that data-driven approach DirectMap quantitatively outperforms the two other methods. In fact, we observe an absolute activation time error and a correlation coefficient, respectively, equal to 7.20 ms, 93.2% using DirectMap, 14.60 ms, 76.2% using FEM-L1 and 13.58 ms, 79.6% using SATDNN-AT. In addition, results show that data-driven approaches (DirectMap and SATDNN-AT) are strongly robust against additive gaussian noise compared to FEM-L1.

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