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Discovering Cardiac Action Potential Model Equations Using Sparse Identification Of Nonlinear Dynamics.

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Discovering Cardiac Action Potential Model Equations Using Sparse Identification Of Nonlinear Dynamics.

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Discovering Cardiac Action Potential Model Equations Using Sparse Identification of Nonlinear Dynamics.

Cole S Welch¹, Elizabeth M Cherry¹

¹Georgia Institute of Technology, Atlanta, GA, USA.

Computing in Cardiology

|April 10, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Sparse Identification of Nonlinear Dynamics (SINDy) effectively reproduces cardiac action potential models. This data-driven approach balances model complexity and accuracy for fitting cardiac AP data.

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Area of Science:

Computational Biology
Mathematical Modeling
Cardiac Electrophysiology

Background:

Cardiac action potential (AP) models are crucial for understanding heart function.
Identifying accurate equations and parameters for AP models remains a significant challenge.
Existing models often struggle to precisely match experimental data.

Purpose of the Study:

To evaluate the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) approach for modeling cardiac action potentials.
To assess SINDy's ability to identify differential equations and parameters from synthetic AP data.
To determine SINDy's performance with various cardiac-specific models and complex dynamics.

Main Methods:

Utilized SINDy, a sparse regression technique, to identify differential equation models from synthetic AP data.

Applied SINDy to two-variable polynomial models, including the FitzHugh-Nagumo (FHN) model and its cardiac variants.

Tested SINDy's capability in fitting data with time-dependent stimulus currents and alternans dynamics.

Main Results:

SINDy successfully reproduced the underlying equations for all tested FHN-based cardiac models.
Cardiac variants showed higher sensitivity to parameter choices and optimizer settings compared to the baseline FHN model.
SINDy demonstrated proficiency in identifying models even with the inclusion of time-varying stimulus currents.

Conclusions:

SINDy is a promising data-driven method for developing accurate and parsimonious cardiac AP models.
The approach offers a robust framework for matching differential equations to experimental cardiac electrophysiology data.
SINDy provides a valuable tool for balancing model complexity and predictive accuracy in cardiac modeling.