Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Electrophysiology of Normal Cardiac Rhythm

Electrophysiology of Normal Cardiac Rhythm

The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase...

Cardiac Action Potential

Cardiac Action Potential

Cardiac action potentials are essential for proper heart function, enabling the rhythmic contractions needed for adequate blood circulation. Nodal cells and Purkinje fibers, specialized for electrical conduction, generate these action potentials.
The cardiac action potential process involves a series of phases characterized by the movement of ions across the cardiac cell membranes, leading to the depolarization and repolarization of the cardiac myocytes.
Ionic Basis of Cardiac Action Potentials

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Analytical interpretation of hemodynamic data in patients with intracardiac shunts: Role of mathematical modeling.

International journal of cardiology. Congenital heart disease·2026

Same author

VENI, VINDy, VICI: A generative reduced-order modeling framework with uncertainty quantification.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Coupling Models of Resistive Valves to Muscle Mechanics in Cardiac Fluid-Structure Interaction Simulations.

International journal for numerical methods in biomedical engineering·2025

Same author

Reduced order modeling with shallow recurrent decoder networks.

Nature communications·2025

Same author

A reduced 3D-0D fluid-structure interaction model of the aortic valve that includes leaflet curvature.

Biomechanics and modeling in mechanobiology·2025

Same author

Defining myocardial fiber bundle architecture in atrial digital twins.

Computers in biology and medicine·2025

Search research articles

Related Experiment Video

Updated: Dec 7, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Deep learning-based reduced order models in cardiac electrophysiology.

Stefania Fresca¹, Andrea Manzoni¹, Luca Dedè¹

¹MOX - Dipartimento di Matematica, Politecnico di Milano, Milano, Italy.

|October 1, 2020

Summary

Deep learning models offer efficient reduced-order models (ROMs) for cardiac electrophysiology simulations. This approach accurately predicts heart electrical behavior, outperforming traditional methods for complex pathological cases.

More Related Videos

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Related Experiment Videos

Last Updated: Dec 7, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Area of Science:

Computational Biology
Biophysics
Medical Imaging

Background:

Cardiac electrophysiology models simulate heart electrical activity using coupled nonlinear dynamical systems.
These models describe the cardiac action potential and ionic variables, crucial for clinical outputs like activation maps.
Conventional reduced-order models (ROMs) struggle with the low regularity and nonlinearity of these systems.

Purpose of the Study:

To develop novel nonlinear reduced-order models (ROMs) for cardiac electrophysiology.
To leverage deep learning (DL) algorithms for efficient and accurate numerical solutions.
To enable multi-scenario analysis in pathological cardiac conditions.

Main Methods:

Utilized deep feedforward neural networks and convolutional autoencoders for ROM construction.
Developed a deep learning-based ROM (DL-ROM) framework.
Investigated four challenging test cases in cardiac electrophysiology.

Main Results:

The DL-ROM framework efficiently provides solutions to parametrized electrophysiology problems.
Demonstrated accurate and efficient ROMs with dimensionality matching system parameters.
DL-ROM significantly outperformed classical projection-based ROMs in test cases.

Conclusions:

Deep learning offers a powerful nonlinear approach for cardiac electrophysiology modeling.
DL-ROM enables efficient multi-scenario analysis, particularly in pathological cases.
This framework advances the numerical simulation of complex cardiac electrical dynamics.