A compact matrix model for atrial electrograms for tissue conductivity estimation

Abdi, Bahareh; Hendriks, Richard C.; Veen, Alle-Jan van der; Groot, Natasja M.S. de

doi:10.1016/j.compbiomed.2019.02.012

Cited by 13 publications

(6 citation statements)

References 23 publications

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“…The isotropic tissues, are stimulated with a stimulation current injected to the cell at the left bottom of the tissue. The initial sampling period for modeling propagation is T s =0.02 ms, but the final electrograms are down-sampled to have a sampling period of T s =0.2 ms. A more detailed description of the simulation steps and parameters can be found in [13]. The employed conductivity maps and the resulting true activation maps are shown in the first and second row of Fig.…”

Section: Resultsmentioning

confidence: 99%

Local Activation Time Estimation in Fractionated Electrograms of Cardiac Mappings

Abdi

Veen

Groot

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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In this study, we propose a novel approach for estimation of local activation times (LATs) in fractionated electrograms. Using an electrophysiological tissue model, we first formulate the electrogram array as a convolution of transmembrane currents with a distance kernel. These currents are more local activities and less affected by the heterogeneity in the tissue compared to electrograms. We then deconvolve the distance kernel with the electrograms to reconstruct the transmembrane current. To stabilize the solution of this illposed deconvolution, we use spatio-temporal total variation as a regularization. This helps to preserve sharp spatial and temporal deflections in the currents that are of higher importance in LAT estimation. Finally, the maximum negative slope of the reconstructed transmembrane currents are used to estimate the LATs. Instrumental comparison to two reference approaches shows that the proposed approach performs better in estimating the LATs in fractionated electrograms.

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Section: Resultsmentioning

confidence: 99%

Local Activation Time Estimation in Fractionated Electrograms of Cardiac Mappings

Abdi

Veen

Groot

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…The fibre direction estimation method by Roney et al [5] is based on applying elliptical wavefront fitting to an activation map directly. Because wavefronts are not elliptical of shape in the presence conduction blocks, this method does not work in the context of AF and the conductivity estimation methods of [2]- [4]. Another fibre direction estimation method has been used by Houben et al [6].…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, most estimation approaches focus on finding a single set of homogeneous parameters for the whole tissue, which does not facilitate localisation of areas with impaired conductivity. Recently, some methods have been developed that estimate conductivity parameters locally [2]- [4]. Although these methods perform relatively well, they depend on a number of parameters that are assumed to be known.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Cardiac Fibre Direction Based on Activation Maps

Vries

Ming

Groot

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Estimating tissue conductivity parameters from electrograms (EGMs) could be an important tool for diagnosing and treating heart rhythm disorders such as atrial fibrillation (AF). One of these parameters is the fibre direction, often assumed to be known in conductivity estimation methods. In this paper, a novel method to estimate the fibre direction from EGMs is presented. This method is based on local conduction slowness vectors of a propagating activation wave. These conduction slowness vectors follow an elliptical pattern that depends on the underlying conductivity parameters. The fibre direction and conductivity anisotropy ratio can therefore be estimated by fitting an ellipse to the conduction slowness vectors. Applying the presented method on simulated data shows that it can estimate the fibre direction more accurately than existing methods, and that its performance depends mostly on the range of wavefront directions present in the measurement area. The main advantage of the presented method is that it still functions relatively well in the presence of conduction blocks, as long as the surrounding tissue is approximately homogeneous.

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“…The unipolar pseudo-EGMs were computed as potentials generated by current sources at a distance r from electrodes [31,32]:…”

Section: Electrogram Modelmentioning

confidence: 99%

Computer based method for identification of fibrotic scars from electrograms and local activation times on the epi- and endocardial surfaces of the ventricles

Okenov,

Nezlobinsky,

Zeppenfeld

et al. 2024

PLoS ONE

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Cardiac fibrosis stands as one of the most critical conditions leading to lethal cardiac arrhythmias. Identifying the precise location of cardiac fibrosis is crucial for planning clinical interventions in patients with various forms of ventricular and atrial arrhythmias. As fibrosis impedes and alters the path of electrical waves, detecting fibrosis in the heart can be achieved through analyzing electrical signals recorded from its surface. In current clinical practices, it has become feasible to record electrical activity from both the endocardial and epicardial surfaces of the heart. This paper presents a computational method for reconstructing 3D fibrosis using unipolar electrograms obtained from both surfaces of the ventricles. The proposed method calculates the percentage of fibrosis in various ventricular segments by analyzing the local activation times and peak-to-peak amplitudes of the electrograms. Initially, the method was tested using simulated data representing idealized fibrosis in a heart segment; subsequently, it was validated in the left ventricle with fibrosis obtained from a patient with nonischemic cardiomyopathy. The method successfully determined the location and extent of fibrosis in 204 segments of the left ventricle model with an average error of 0.0±4.3% (N = 204). Moreover, the method effectively detected fibrotic scars in the mid-myocardial region, a region known to present challenges in accurate detection using electrogram amplitude as the primary criterion.

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A compact matrix model for atrial electrograms for tissue conductivity estimation

Cited by 13 publications

References 23 publications

Local Activation Time Estimation in Fractionated Electrograms of Cardiac Mappings

Local Activation Time Estimation in Fractionated Electrograms of Cardiac Mappings

Estimation of Cardiac Fibre Direction Based on Activation Maps

Computer based method for identification of fibrotic scars from electrograms and local activation times on the epi- and endocardial surfaces of the ventricles

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