Usage of spline interpolation in catheter-based cardiac mapping

Yılmaz, Bülent; Cunedioglu, Ugur; Baysoy, Engin

doi:10.3906/elk-0911-277

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…The basis functions then have the form R i ( x ) := R ( d ( x , x i )), depending only on the distance d ( x , x i ) and usually hyperparameters that control the RBF shape (depending on the choice of basis function). RBFs are used for LAT interpolation by both [ 63 ] and [ 64 ]. Both use ‘polyharmonic splines’, consisting of RBFs with additional polynomial terms, as follows: …”

Section: Global Methodsmentioning

confidence: 99%

“…Both [ 63 ] and [ 64 ] used Euclidean distances, which cannot account for the manifold, and interpolating conditions, which cannot account for observation error. However, we note the following: (i) to account for the manifold, Euclidean distances can be replaced with geodesic distances and the polynomial term dropped entirely (this term is usually included to improve extrapolation, where CV estimation is undoubtedly very poor); (ii) to account for noisy observations, smoothing interpolation is possible using regularized least squares; in fact the posterior distribution can be easily obtained — see Appendix A .…”

Section: Global Methodsmentioning

confidence: 99%

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Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate

Coveney

Cantwell

Roney

2022

Med Biol Eng Comput

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Characterizing patient-specific atrial conduction properties is important for understanding arrhythmia drivers, for predicting potential arrhythmia pathways, and for personalising treatment approaches. One metric that characterizes the health of the myocardial substrate is atrial conduction velocity, which describes the speed and direction of propagation of the electrical wavefront through the myocardium. Atrial conduction velocity mapping algorithms are under continuous development in research laboratories and in industry. In this review article, we give a broad overview of different categories of currently published methods for calculating CV, and give insight into their different advantages and disadvantages overall. We classify techniques into local, global, and inverse methods, and discuss these techniques with respect to their faithfulness to the biophysics, incorporation of uncertainty quantification, and their ability to take account of the atrial manifold. Graphical abstract

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Section: Global Methodsmentioning

confidence: 99%

Section: Global Methodsmentioning

confidence: 99%

Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate

Coveney

Cantwell

Roney

2022

Med Biol Eng Comput

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“…Various interpolation schemes have already been reviewed, along with their effects on EAM. 37,[45][46][47][48] A comparison of current techniques aimed at the reconstruction of activation maps using sparse EAM recordings is shown in Table 2. 44,[49][50][51][52] Conduction Velocity Estimation…”

Section: Interpolation Of Activation Time Mapsmentioning

confidence: 99%

A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data

Jaffery,

Melki,

Slabaugh

et al. 2024

Arrhythm Electrophysiol Rev

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Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data.

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“…Current interpolation methods include cubic spline interpolation [14], radial basis functions [15], Gaussian processes [16], [17], physics-informed neural networks [18], [19], and graph convolutional neural networks [20]. Of these, [14] and [15] do not interpolate on the manifold surface, while [16] and [17] assume a smooth Gaussian prior on a transformation of the data.…”

Section: Introductionmentioning

confidence: 99%

“…The need for re-examination of interpolation methods for LAT mapping has already been noted in the literature [ 12 ], [ 13 ], but presently only a few groups have attempted to address it. Current interpolation methods include cubic spline interpolation [ 14 ], radial basis functions [ 15 ], Gaussian processes [ 16 ], [ 17 ], physics-informed neural networks [ 18 ], [ 19 ], and graph convolutional neural networks [ 20 ]. Of these, [ 14 ] and [ 15 ] do not interpolate on the manifold surface, while [ 16 ] and [ 17 ] assume a smooth Gaussian prior on a transformation of the data.…”

Section: Introductionmentioning

confidence: 99%

Manifold Approximating Graph Interpolation of Cardiac Local Activation Time

Hellar

Cosentino²,

John³

et al. 2022

IEEE Trans. Biomed. Eng.

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Local activation time (LAT) mapping of cardiac chambers is vital for targeted treatment of cardiac arrhythmias in catheter ablation procedures. Current methods require too many LAT observations for an accurate interpolation of the necessarily sparse LAT signal extracted from intracardiac electrograms (EGMs). Additionally, conventional performance metrics for LAT interpolation algorithms do not accurately measure the quality of interpolated maps. We propose, first, a novel method for spatial interpolation of the LAT signal which requires relatively few observations; second, a realistic sub-sampling protocol for LAT interpolation testing; and third, a new color-based metric for evaluation of interpolation quality that quantifies perceived differences in LAT maps. Methods: We utilize a graph signal processing framework to reformulate the irregular spatial interpolation problem into a semi-supervised learning problem on the manifold with a closed-form solution. The metric proposed uses a color difference equation and color theory to quantify visual differences in generated LAT maps. Results: We evaluate our approach on a dataset consisting of seven LAT maps from four patients obtained by the CARTO electroanatomic mapping system during premature ventricular complex (PVC) ablation procedures. Random sub-sampling and reinterpolation of the LAT observations show excellent accuracy for relatively few observations, achieving on average 6% lower error than state-of-the-art techniques for only 100 observations. Conclusion: Our study suggests that graph signal processing methods can improve LAT mapping for cardiac ablation procedures. Significance: The proposed method can reduce patient time in surgery by decreasing the number of LAT observations needed for an accurate LAT map.

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Usage of spline interpolation in catheter-based cardiac mapping

Cited by 6 publications

References 22 publications

Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate

Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate

A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data

Manifold Approximating Graph Interpolation of Cardiac Local Activation Time

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