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Non-invasive personalisation of cardiac electrophysiological models from surface electrograms

Sophie Giffard-Roisin 1
1 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The objective of this thesis is to use non-invasive data (body surface potential mapping, BSPM) to personalise the main parameters of a cardiac electrophysiological (EP) model for predicting the response to cardiac resynchronization therapy (CRT). CRT is a clinically proven treatment option for some heart failures. However, these therapies are ineffective in 30% of the treated patients and involve significant morbidity and substantial cost. The precise understanding of the patient-specific cardiac function can help to predict the response to therapy. Until now, such methods required to measure intra-cardiac electrical potentials through an invasive endovascular procedure which can be at risk for the patient. We developed a non-invasive EP model personalisation based on a patient-specific simulated database and machine learning regressions. First, we estimated the onset activation location and a global conduction parameter. We extended this approach to multiple onsets and to ischemic patients by means of a sparse Bayesian regression. Moreover, we developed a reference ventricle-torso anatomy in order to perform an common offline regression and we predicted the response to different pacing conditions from the personalised model. In a second part, we studied the adaptation of the proposed method to the input of 12-lead electrocardiograms (ECG) and the integration in an electro-mechanical model for a clinical use. The evaluation of our work was performed on an important dataset (more than 25 patients and 150 cardiac cycles). Besides having comparable results with state-of-the-art ECG imaging methods, the predicted BSPMs show good correlation coefficients with the real BSPMs.
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Submitted on : Thursday, April 19, 2018 - 3:19:08 PM
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  • HAL Id : tel-01771476, version 1



Sophie Giffard-Roisin. Non-invasive personalisation of cardiac electrophysiological models from surface electrograms. Other. Université Côte d'Azur, 2017. English. ⟨NNT : 2017AZUR4092⟩. ⟨tel-01771476⟩



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