Sparse Bayesian Non-linear Regression for Multiple Onsets Estimation in Non-invasive Cardiac Electrophysiology
| Autor: | Nicholas Ayache, Maxime Sermesant, Jack Lee, Lauren Fovargue, Reza Razavi, Hervé Delingette, Tom Jackson, Sophie Giffard-Roisin, C. Aldo Rinaldi |
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| Přispěvatelé: | COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Mihaela Pop, SOFA, European Project: 611823,EC:FP7:ICT,FP7-ICT-2013-10,VP2HF(2013), European Project: 291080,EC:FP7:ERC,ERC-2011-ADG_20110209,MEDYMA(2012) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Computer science
Personalisation [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging 0206 medical engineering Bayesian probability 02 engineering and technology Machine learning computer.software_genre 030218 nuclear medicine & medical imaging Relevance vector machine 03 medical and health sciences 0302 clinical medicine [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system [INFO.INFO-IM]Computer Science [cs]/Medical Imaging Sinus rhythm Ground truth Cardiac electrophysiology business.industry Pattern recognition 020601 biomedical engineering [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Regression Kernel (statistics) Relevance Vector Machine ECG Imaging Artificial intelligence Cardiac Electrophysiology business Nonlinear regression computer |
| Zdroj: | Functional imaging and modelling of the heart 2017 Functional imaging and modelling of the heart 2017, Jun 2017, Toronto, Canada. pp.230-238, ⟨10.1007/978-3-319-59448-4_22⟩ Functional Imaging and Modelling of the Heart ISBN: 9783319594477 FIMH |
| DOI: | 10.1007/978-3-319-59448-4_22⟩ |
| Popis: | Best paper award FIMH 2017, category: Electrophysiology; International audience; 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 per-sonalisation 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 18.4mm) demonstrate the usefulness of this non-linear approach. |
| Databáze: | OpenAIRE |
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