Role of Myocardial Properties and Pacing Lead Location on ECG in Personalized Paced Heart Models

Ushenin, Konstantin; Dokuchaev, Arseniy; Magomedova, S.M.; Сопов, О. В.; Kalinin, Vitaly; Solovyova, Olga

doi:10.22489/cinc.2017.227-432

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…In particular, this approach correctly includes the far-field effect. The used model was verified against clinical data of electrocardiography with 224 leads during activation of the myocardium from a point [15]. We suppose that model complexity and a wide representation of physiological features make the model suitable for the generation of the test dataset.…”

Section: Methodsmentioning

confidence: 99%

Phase Mapping for Cardiac Unipolar Electrograms with Neural Network Instead of Phase Transformation

Ushenin

Nesterova

Smarko

et al. 2020

2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

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Digital signal processing can be performed in two major ways. The first is a pipeline of signal transformations, and the second is machine learning approaches that require tagged data for training. This paper studies the third way. We generate a training dataset for a neural network in a series of numerical experiments and uses the trained neural network for processing of new signals. The current work focuses on phase mapping, which is an approach of cardiac unipolar electrogram processing, that helps to analyze complex non-stationary behavior of cardiac arrhythmias. Idealized models of 1D myocardial electrophysiology provide a training dataset. Then, the convolution neural network is trained to provide phase-like transformation for the phase mapping. The proposed approaches were validated against data from the detailed personalized model of human torso electrophysiology. The current paper includes a visualization of the phase map based on our approach and shows the robustness of the proposed approaches in the analysis of the complex nonstationary periodic activity of the excitable cardiac tissue.

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Section: Methodsmentioning

confidence: 99%

Phase Mapping for Cardiac Unipolar Electrograms with Neural Network Instead of Phase Transformation

Ushenin

Nesterova

Smarko

et al. 2020

2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

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“…Transmural and apico-basal cellular heterogeneities were simulated using the approaches proposed in Keller et al (2012) and ten Tusscher and Panfilov (2006), respectively. Simulations of cardiac electrical activity were performed with the methodology described in Ushenin et al (2017) using the Cardiac CHASTE software (Mirams et al, 2013). In each anatomical patient model, these finite-element calculations resulted in the transmembrane potentials in the myocardial volume, while the electrical potentials were simulated at each node of the tetrahedral meshes.…”

Section: Methodsmentioning

confidence: 99%

Solving the Inverse Problem of Electrocardiography on the Endocardium Using a Single Layer Source

Kalinin¹,

Potyagaylo²,

Kalinin³

2019

Front. Physiol.

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The inverse problem of electrocardiography consists in reconstructing cardiac electrical activity from given body surface electrocardiographic measurements. Despite tremendous progress in the field over the last decades, the solution of this problem in terms of electrical potentials on both epi- and the endocardial heart surfaces with acceptable accuracy remains challenging. This paper presents a novel numerical approach aimed at improving the solution quality on the endocardium. Our method exploits the solution representation in the form of electrical single layer densities on the myocardial surface. We demonstrate that this representation brings twofold benefits: first, the inverse problem can be solved for the physiologically meaningful single layer densities. Secondly, a conventional transfer matrix for electrical potentials can be split into two parts, one of which turned out to posess regularizing properties leading to improved endocardial reconstructions. The method was tested in-silico for ventricular pacings utilizing realistic CT-based heart and torso geometries. The proposed approach provided more accurate solution on the ventricular endocardium compared to the conventional potential-based solutions with Tikhonov regularization of the 0th, 1st, and 2nd orders. Furthermore, we show a uniform spatio-temporal behavior of the single layer densities over the heart surface, which could be conveniently employed in the regularization procedure.

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“…The simulation approach was described in our previous work [5]. Briefly, personalized geometry model of the human heart and torso were created using computed tomography data of a patient with the dilated cardiomyopathy.…”

Section: Electrophysiology Modelmentioning

confidence: 99%

In Silico Comparison of Phase Maps Based on Action Potential and Extracellular Potential

Ushenin

Razumov

Kalinin

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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In this work, a computer simulation of the reentrant ventricular tachycardia (VT) was used to investigate the peculiar properties of phase maps based on transmembrane potentials (TP) and extracellular potentials (EP). The simulation approach included the bidomain model with full myocardium-torso coupling, a realistic ionic model of the human cardiomyocytes and a personalized geometry of the heart and torso. The phase mapping pipeline includes a signal detrending and the Hilbert transform. It was demonstrated that TP-based phase maps correlated well with the propagation of cardiac excitation. In contrast, EP-based phase mapping provides some aberrations which can complicate electrophysiological interpretation of the phase maps in terms of cardiac activation sequence. It was also shown that a modification of the phase computation algorithm, including the sign inversion of signals and a special transformation of the phase plot, can partially eliminate these aberrations and make EP-based phase maps resemble TP-based maps.

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Role of Myocardial Properties and Pacing Lead Location on ECG in Personalized Paced Heart Models

Cited by 4 publications

References 7 publications

Phase Mapping for Cardiac Unipolar Electrograms with Neural Network Instead of Phase Transformation

Phase Mapping for Cardiac Unipolar Electrograms with Neural Network Instead of Phase Transformation

Solving the Inverse Problem of Electrocardiography on the Endocardium Using a Single Layer Source

In Silico Comparison of Phase Maps Based on Action Potential and Extracellular Potential

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