Year: 2023
Type: conference-paper
Abstract: The study of arrhythmia formation is impeded by limitations in sensor technology, since not all quantities of interest can be measured directly from the same cell or tissue preparation.Data assimilation algorithms can reconstruct unmeasured quantities by combining model predictions with available data.However, it is not clear which types of measurements are best for reconstructing data, or how abnormal action-potential patterns, such as alternans, affect the informativeness of measurements.To address these issues, we examined the Shiferaw-Sato-Karma (SSK) cardiac myocyte model, which can be used to simulate multiple alternans mechanisms.We conducted a numerical study in which each SSK dynamical variable was considered to be a source of simulated data, and computed observability measures, where observability is a controltheoretic model property that indicates whether unmeasured quantities can be reconstructed from a measured variable.Although the best measurements (in the sense of maximizing observability) varied depending on alternans mechanism (voltage-or calcium-driven), we found that some patterns held for both mechanisms, such as intracellular calcium concentration yielding stronger observability than membrane potential.Observability strengths also typically predicted the relative performances of Kalmanfilter-based assimilators for different measurement types.
Source: Computing in cardiology
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Funder: National Science Foundation
Awards: CPS: Frontier: Collaborative Research: Compositional, Approximate, and Quantitative Reasoning for Medical Cyber-Physical Systems, REU Site: Extremal Graph Theory and Dynamical Systems at RIT, CPS: Frontier: Collaborative Research: Compositional, Approximate, and Quantitative Reasoning for Medical Cyber-Physical Systems
OpenAlex ID: W4390446763