title: A cardiac electrical activity model based on a cellular automata system in comparison with neural network model.
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.
authors.affiliation: Department of Computer Science, University of Karachi, Karachi, Pakistan.
authors.forename: Muhammad Sadiq Ali
authors.initials: MS
authors.lastname: Khan
authors.affiliation: Department of Computer Science, University of Karachi, Karachi, Pakistan.
authors.forename: Sidrah
authors.initials: S
authors.lastname: Yousuf
mesh_terms: D000200:Action Potentials; D000818:Animals; D001145:Arrhythmias, Cardiac / Q000503:physiopathology*; D003198:Computer Simulation; D006329:Heart Conduction System / Q000503:physiopathology*; D006339:Heart Rate; D006801:Humans; D008955:Models, Cardiovascular; D016571:Neural Networks, Computer; D013997:Time Factors