TY - CHAP
T1 - Sparse bayesian non-linear regression for multiple onsets estimation in non-invasive cardiac electrophysiology
AU - Giffard-Roisin, Sophie
AU - Delingette, Hervé
AU - Jackson, Tom
AU - Fovargue, Lauren
AU - Lee, Jack
AU - Rinaldi, Aldo
AU - Ayache, Nicholas
AU - Razavi, Reza
AU - Sermesant, Maxime
PY - 2017/5/23
Y1 - 2017/5/23
N2 - 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.
AB - 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.
KW - Cardiac electrophysiology
KW - ECG imaging
KW - Personalisation
KW - Relevance Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=85020439141&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-59448-4_22
DO - 10.1007/978-3-319-59448-4_22
M3 - Conference paper
AN - SCOPUS:85020439141
SN - 9783319594477
VL - 10263 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 230
EP - 238
BT - Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings
PB - Springer Verlag
T2 - 9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017
Y2 - 11 June 2017 through 13 June 2017
ER -