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Variability modeling and numerical biomarkers design in cardiac electrophysiology

Eliott Tixier 1, 2
1 REO - Numerical simulation of biological flows
LJLL - Laboratoire Jacques-Louis Lions, UPMC - Université Pierre et Marie Curie - Paris 6, Inria de Paris
Abstract : This PhD thesis is dedicated to the study of the variability observed in cardiac electrophysiology (i.e. the electrical activity of biological tissues) measurements and to the design of numerical biomarkers extracted from these measurements. The potential applications are numerous, ranging from a better understanding of existing electrophysiology models to the assessment of adverse effects of drugs or the diagnosis of cardiac pathologies. The cardiac electrophysiology models considered in the present work are either ODEs or PDEs depending on whether we focus on the cell scale or the tissue scale. In both cases, these models are highly non-linear and computationally intensive. We proceed as follows: first we develop numerical tools that address general issues and that are applicable beyond the scope of cardiac electrophysiology. Then, we apply those tools to synthetic electrophysiology measurements in various realistic scenarios and, when available, to real experimental data. In the first part of this thesis, we present a general method for estimating the probability density function (PDF) of uncertain parameters of models based on ordinary differential equations (ODEs) or partial differential equations (PDEs). The method is non-instrusive and relies on offline evaluations of the forward model, making it computationally cheap in practice compared to more sophisticated approaches. The method is illustrated with generic PDE and ODE models. It is then applied to synthetic and experimental electrophysiology measurements. In the second part of this thesis, we present a method to extract and select biomarkers from models outputs in view of performing classication tasks or solving parameter identification problems. The method relies on the resolution of a sparse optimization problem. The method is illustrated with simple models and then applied to synthetic measurements, including electrocardiogram recordings, and to experimental data obtained from micro-electrode array measurements.
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Submitted on : Tuesday, January 30, 2018 - 11:34:41 PM
Last modification on : Thursday, March 26, 2020 - 9:27:06 PM
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  • HAL Id : tel-01697112, version 1



Eliott Tixier. Variability modeling and numerical biomarkers design in cardiac electrophysiology. Optimization and Control [math.OC]. Université Pierre & Marie Curie - Paris 6, 2017. English. ⟨tel-01697112v1⟩



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