Effect of interpolation on electroanatomical mapping

Sanromán‐Junquera, Margarita; Diaz-Valencia, Raquel; García‐Alberola, Arcadio; Rojo‐Álvarez, José Luis; Jiménez, Inmaculada

doi:10.1109/cic.2015.7408661

Cited by 4 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Introductionmentioning

confidence: 99%

“…In this work, a Support Vector Machine (SVM) algorithm with autocorrelation (AC) kernel has been chosen as interpolation method because of its promising results versus other interpolation methods in the presence of nonuniform data [2]. The effect of working in 2D is also studied, and compared to 3D Cartesian coordinates of the spatial locations, so that the corresponding values of an electrical feature measured on those 3D spatial locations were transformed to spherical coordinates and then the radius coordinate was discarded.…”

Section: Introductionmentioning

confidence: 99%

“…Given that the electrical feature is measured only in a few spatial locations, an interpolation algorithm is needed to estimate the feature value at points with no measure and build the map from those samples. Precision in the diagnosis and treatment of arrhythmias using CNS is directly related to the quality of the map, and hence to the interpolation method to be considered [2].In this work, a Support Vector Machine (SVM) algorithm with autocorrelation (AC) kernel has been chosen as interpolation method because of its promising results versus other interpolation methods in the presence of nonuniform data [2]. The effect of working in 2D is also studied, and compared to 3D Cartesian coordinates of the spatial locations, so that the corresponding values of an electrical feature measured on those 3D spatial locations were transformed to spherical coordinates and then the radius coordinate was discarded.…”

mentioning

confidence: 99%

“…Non-uniform interpolation with SVM Several previous works studied the influence of the interpolation methods in the quality of cardiac electroanatomical maps [2,3]. In [2], Triangulated Irregular Network (TIN), Thin Plate Spline (TPS), and SVM with Radial Basis Function (RBF) kernel were studied for anatomical and electro-anatomical maps interpolation.…”

mentioning

confidence: 99%

“…In [2], Triangulated Irregular Network (TIN), Thin Plate Spline (TPS), and SVM with Radial Basis Function (RBF) kernel were studied for anatomical and electro-anatomical maps interpolation. That work concluded that TPS, followed by SVM with RBF kernel, yielded the best results in terms of precision and computational cost.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Nonuniform Interpolation of Cardiac Navigation Maps Using Support Vector Machines

Castro-Garcia

Sanromán‐Junquera

Guerrero-Curieses

et al. 2016

2016 Computing in Cardiology Conference (CinC)

Self Cite

View full text Add to dashboard Cite

A new method for non-uniform interpolation of electroanatomical cardiac maps from Cardiac Navigation Systems (CNS) is here proposed and benchmarked. We adapted the equations of support vector machines for estimation problems in terms of the two angular dimensions azimuth and elevation and used an autocorrelation kernel. Moreover, the influence of the number of spatial locations, its minimum number to obtain a map that precisely replicates the original or gold-standard and the effect of working in 2D from 3D were also studied. Two basic simulation scenarios were used: (a) a prolate semi-ellipsoid, yielding a geometry similar to the ventricular chamber, with different width pulse and Gaussian activations; and (b) detailed simulated models of cardiac activity in the atria. Results were compared with those obtained with other interpolation methods. In the Gaussian and pulse-like activations the largest decrease in mean absolute error (MAE) for the test set was achieved by using 150 spatial locations (MAE from 0.007 to 0.117). In the simulation models the error stabilized at 500 spatial locations (MAE from 0,002 to 0.014). The proposed method can provide improved quality for electro-anatomical maps interpolation. IntroductionArrhythmias are alterations of normal cardiac rhythm, which cause the heart to abnormally beat too fast, too slow, or with an irregular rhythm [1]. Cardiac Navigation Systems (CNS) allow replicating the patient cardiac anatomy in a computer system, addressing catheters precisely within the heart, and registering the cardiac electrical activity. The integration of both anatomical and electric data compounds electro-anatomical maps of the cardiac chamber, in which a compromise between map quality and its acquisition time is required. Given that the electrical feature is measured only in a few spatial locations, an interpolation algorithm is needed to estimate the feature value at points with no measure and build the map from those samples. Precision in the diagnosis and treatment of arrhythmias using CNS is directly related to the quality of the map, and hence to the interpolation method to be considered [2].In this work, a Support Vector Machine (SVM) algorithm with autocorrelation (AC) kernel has been chosen as interpolation method because of its promising results versus other interpolation methods in the presence of nonuniform data [2]. The effect of working in 2D is also studied, and compared to 3D Cartesian coordinates of the spatial locations, so that the corresponding values of an electrical feature measured on those 3D spatial locations were transformed to spherical coordinates and then the radius coordinate was discarded.The data used to evaluate the algorithm under study are a set of four simple features over the surface of a prolate semiellipsoid and some activation time maps obtained from two simulations which replicate the structure of maps generated by a CNS.The paper is organized as follows. Section 2 contains the details of the SVM interpolation algorithm considered. In Sect...

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Nonuniform Interpolation of Cardiac Navigation Maps Using Support Vector Machines

Castro-Garcia

Sanromán‐Junquera

Guerrero-Curieses

et al. 2016

2016 Computing in Cardiology Conference (CinC)

Self Cite

View full text Add to dashboard Cite

show abstract

A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data

Jaffery,

Melki,

Slabaugh

et al. 2024

Arrhythm Electrophysiol Rev

View full text Add to dashboard Cite

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.

show abstract

Effect of Different Ventricular Arrhythmia Origin on Cardiac Sound Variability Using M-mode Signal Representation

Ortiz-Puente

Sanromán‐Junquera

Muñoz-Romero

et al. 2017

Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

In recent years, interest has been paid to the identification of the arrhythmia anatomical origin in ventricular tachycardia from available information in the patient, including ECG morphology and electrograms stored in implantable devices. In this work, we scrutinize the differences in morphology from cardiac sounds. For this purpose, we compared the spatial and temporal similarity in cardiac sounds recorded during electrical intracardiac stimulation, in terms of the so-called M-mode representation for cardiac signals. A protocol was set in 5 patients referred to electrophysiological study. Cardiac pacing was made at 400 ms from different points in the left ventricle. During stimulation, we recorded: (1) the ECG signals from 12 leads; (2) the spatial coordinates for the pacing site, by using an intracardiac navigation system; (3) the heart sounds, by using a high-fidelity recording system. After visual-supervised correction, M-mode maps represented an aligned and clearly represented the variations due to distance and substrate in a consistent view. The same signal processing was followed for the cardiac sounds, showing that the ECG-based alignment was not sufficient, and that the sound signal represents a very much lower signal to noise ratio domain.

show abstract

Effect of interpolation on electroanatomical mapping

Cited by 4 publications

References 9 publications

Nonuniform Interpolation of Cardiac Navigation Maps Using Support Vector Machines

Nonuniform Interpolation of Cardiac Navigation Maps Using Support Vector Machines

A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data

Effect of Different Ventricular Arrhythmia Origin on Cardiac Sound Variability Using M-mode Signal Representation

Contact Info

Product

Resources

About