Key result
The StreaMRAK kernel method estimated cardiac action potential model parameters with higher accuracy and less sensitivity to parameter identifiability problems than existing KRR solvers and nearest-neighbor heuristics.
Why the study?
Current methods for solving inverse problems in cardiac electrophysiology are limited by their accuracy, scalability, practicality, or a combination of these.
The StreaMRAK kernel method improves the accuracy and scalability of solving inverse problems in cardiac electrophysiology, potentially enhancing drug effect identification in in vitro systems.
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May refine cardiac model parameterization; leaves open clinical validation before practice adoption.
Current methods for solving inverse problems in cardiac electrophysiology are limited by their accuracy, scalability, practicality, or a combination of these. In this proof-of-concept study we demonstrate the feasibility of using kernel methods to solve the inverse problem of estimating the parameters of ionic membrane currents from observations of corresponding action potential (AP) traces. In particular, we consider AP traces generated by a cardiac cell action potential model, which mimics those obtained experimentally in measurable in vitro cardiac systems. Using synthetic training data from the 1977 Beeler-Reuter AP model of mammalian ventricular cardiomyocytes, we demonstrate our recently proposed boosted kernel ridge regression (KRR) solver StreaMRAK, which is particularly robust and well-adapted for high-complexity functions. We show that this method is less memory demanding, estimates the model parameters with higher accuracy, and is less exposed to parameter sensitivity issues than existing methods, such as standard KRR solvers and loss-minimization schemes based on nearest-neighbor heuristics.
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Oslandsbotn et al. (2023) studied Cardiac electrophysiology (synthetic data) (n=6,000). StreaMRAK (boosted KRR solver) vs. FALKON and nearest-neighbor loss-minimization schemes was evaluated on Parameter estimation accuracy (RMSE) and variability. The StreaMRAK kernel method estimated cardiac action potential model parameters with higher accuracy and less sensitivity to parameter identifiability problems than existing KRR solvers and nearest-neighbor heuristics.
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