Sparse bayesian non-linear regression for multiple onsets estimation in non-invasive cardiac electrophysiology

Sophie Giffard-Roisin*, Hervé Delingette, Tom Jackson, Lauren Fovargue, Jack Lee, Aldo Rinaldi, Nicholas Ayache, Reza Razavi, Maxime Sermesant

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

7 Citations (Scopus)


In the scope of modelling cardiac electrophysiology (EP) for understanding pathologies and predicting the response to therapies, patient-specific model parameters need to be estimated. Although personalisation from non-invasive data (body surface potential mapping, BSPM) has been investigated on simple cases mostly with a single pacing site, there is a need for a method able to handle more complex situations such as sinus rhythm with several onsets. In the scope of estimating cardiac activation maps, we propose a sparse Bayesian kernel-based regression (relevance vector machine, RVM) from a large patient-specific simulated database. RVM additionally provides a confidence on the result and an automatic selection of relevant features. With the use of specific BSPM descriptors and a reduced space for the myocardial geometry, we detail this framework on a real case of simultaneous biventricular pacing where both onsets were precisely localised. The obtained results (mean distance to the two ground truth pacing leads is 18.4 mm) demonstrate the usefulness of this non-linear approach.

Original languageEnglish
Title of host publicationFunctional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings
PublisherSpringer Verlag
Number of pages9
Volume10263 LNCS
ISBN (Print)9783319594477
Publication statusE-pub ahead of print - 23 May 2017
Event9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017 - Toronto, Canada
Duration: 11 Jun 201713 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10263 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017


  • Cardiac electrophysiology
  • ECG imaging
  • Personalisation
  • Relevance Vector Machine

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