心脏电生理学
计算机科学
人工智能
噪音(视频)
深度学习
理论计算机科学
机器学习
电生理学
神经科学
生物
图像(数学)
作者
Victoriya Kashtanova,Mihaela Pop,Ibrahim Ayed,Patrick Gallinari,Maxime Sermesant
标识
DOI:10.1007/978-3-031-23443-9_18
摘要
Biophysically detailed mathematical modeling of cardiac electrophysiology is often computationally demanding, for example, when solving problems for various patient pathological conditions. Furthermore, it is still difficult to reduce the discrepancy between the output of idealized mathematical models and clinical measurements, which are usually noisy. In this paper, we propose a fast physics-based deep learning framework to learn cardiac electrophysiology dynamics from data. This novel framework has two components, decomposing the dynamics into a physical term and a data-driven term, respectively. This construction allows the framework to learn from data of different complexity. Using 0D in silico data, we demonstrate that this framework can reproduce the complex dynamics of transmembrane potential even in presence of noise in the data. Additionally, using ex vivo 0D optical mapping data of action potential, we show the ability of our framework to identify the relevant physical parameters for different heart regions.
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