A cardiac electrical activity model based on a cellular automata system in comparison with neural network model
Muhammad Sadiq Ali Khan1, Sidrah Yousuf1
1Department of Computer Science, University of Karachi, Karachi, Pakistan.
Insights
This study introduces a Cellular Automata model to simulate cardiac electrical activity and identify heart rhythm states. The model efficiently distinguishes between normal action potentials and irregular arrhythmias.
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.
Abstract:
Cardiac Electrical Activity is commonly distributed into three dimensions of Cardiac Tissue (Myocardium) and evolves with duration of time. The indicator of heart diseases can occur randomly at any time of a day. Heart rate, conduction and each electrical activity during cardiac cycle should be monitor non-invasively for the assessment of "Action Potential" (regular) and "Arrhythmia" (irregular) rhythms. Many heart diseases can easily be examined through Automata model like Cellular Automata concepts. This paper deals with the different states of cardiac rhythms using cellular automata with the comparison of neural network also provides fast and highly effective stimulation for the contraction of cardiac muscles on the Atria in the result of genesis of electrical spark or wave. The specific formulated model named as "States of automaton Proposed Model for CEA (Cardiac Electrical Activity)" by using Cellular Automata Methodology is commonly shows the three states of cardiac tissues conduction phenomena (i) Resting (Relax and Excitable state), (ii) ARP (Excited but Absolutely refractory Phase i.e. Excited but not able to excite neighboring cells) (iii) RRP (Excited but Relatively Refractory Phase i.e. Excited and able to excite neighboring cells). The result indicates most efficient modeling with few burden of computation and it is Action Potential during the pumping of blood in cardiac cycle.
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