A cardiac electrical activity model based on a cellular automata system in comparison with neural network model

Area of Science:

• Biophysics
• Computational Biology
• Cardiology

Background:

• Cardiac electrical activity is complex, three-dimensional, and time-dependent.
• Monitoring heart rate, conduction, and electrical activity non-invasively is crucial for assessing cardiac health.
• Cellular Automata (CA) offer a promising approach for modeling heart diseases.

Purpose of the Study:

• To model the different states of cardiac rhythms using Cellular Automata.
• To compare the efficacy of a CA model with neural networks for cardiac electrical activity simulation.
• To develop a computational model for understanding cardiac muscle contraction and electrical phenomena.

Main Methods:

• Formulation of a novel model named "States of Automaton Proposed Model for CEA (Cardiac Electrical Activity)" using Cellular Automata Methodology.
• Simulation of three distinct cardiac tissue conduction states: Resting, Absolute Refractory Period (ARP), and Relative Refractory Period (RRP).
• Comparison of the CA model's performance with neural network approaches.

Main Results:

• The proposed CA model efficiently represents cardiac electrical activity and its different states.
• The model demonstrates a low computational burden, offering an efficient method for analysis.
• The model accurately simulates the generation of electrical sparks or waves leading to atrial contraction.

Conclusions:

• Cellular Automata provide an efficient and effective method for modeling cardiac electrical activity and action potentials.
• The developed model accurately captures the physiological states of cardiac tissue conduction.
• This approach aids in the assessment of regular (action potential) and irregular (arrhythmia) heart rhythms.