Automatic reconstruction of activation and velocity maps from electro-anatomic data by radial basis functions

By M. Masè, F. Ravelli. 2010

32 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. F. Sahli Costabal
2. Yibo Yang
3. Paris Perdikaris
4. Ellen Kuhl
5. Daniel E. Hurtado
6. Sam Coveney
7. R. Clayton
8. C. Corrado

Citations in the corpus

• Physics-Informed Neural Networks for Cardiac Activation Mapping (2020, methodology): “The most common approach to interpolate the data is to use linear functions [2, 3] or radial basis functions [4].”
• A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction (2018, unknown): “Both polynomial surface fitting [33] and radial basis function methodologies [34] are suitable for multiple wavefronts and any arrangement of points, but have larger data requirements and may over interpolate data.”
• Probabilistic Interpolation of Uncertain Local Activation Times on Human Atrial Manifolds (2020, methodology): “similar to Radial Basis Function interpolation [5] (we used a GP with RBF kernel) and Spline interpolation [6] (splines are special cases of Gaussian processes).”