Key result
A reduced-order model based on Proper Orthogonal Decomposition efficiently approximated cardiac electrophysiology simulations and restitution curves compared to full-order models.
A reduced-order model using Proper Orthogonal Decomposition can efficiently and accurately simulate cardiac electrophysiology and solve inverse problems for parameter identification.
Facilitates efficient preclinical cardiac electrophysiology modeling; leaves open human validation and clinical translation.
A reduced-order model based on proper orthogonal decomposition (POD) is proposed for the bidomain equations of cardiac electrophysiology. Its accuracy is assessed through electrocardiograms in various configurations, including myocardium infarctions and long-time simulations. We show in particular that a restitution curve can efficiently be approximated by this approach. The reduced-order model is then used in an inverse problem solved by an evolutionary algorithm. Some attempts are presented to identify ionic parameters and infarction locations from synthetic electrocardiograms.
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A 2012 study studied Cardiac electrophysiology (computational simulation). Reduced-order modeling (Proper Orthogonal Decomposition) vs. Full-order model was evaluated on Accuracy of simulated electrocardiograms (ECGs) and restitution curves. A reduced-order model based on Proper Orthogonal Decomposition efficiently approximated cardiac electrophysiology simulations and restitution curves compared to full-order models.
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