Development of neural network model for signs determination of atrial fibrillation for subsystem of cardiorhythmogram signals processing

Abstract

Task of the analysis of a cardio rhythm in real time is detection of early arrhythmias for the purpose of their treatment and prevention of life-endangering arrhythmias. In order to solve the problem of classification of heart rhythm features based on cardiorhythmogram processing, an apparatus of artificial neural networks can be used. One of the most dangerous arrhythmias is atrial fibrillation. Therefore, the development of a neural network model for determining atrial fibrillation features, suitable for implementation on the programmable logic basis, for a subsystem for processing cardiorhythmogram signals is an urgent task. Purpose – development of a neural network model for determining atrial fibrillation features for a signal processing subsystem characterized by high reliability and the implementation possibility on the basis of programmable logic. A neural network model for features determining of atrial fibrillation has been developed, characterized by high reliability and insignificant hardware costs when implemented on field programmable gate arrays (FPGA). Program modeling of neural network model for signs determination of atrial fibrillation is performed. A neural network model for characteristics determining of atrial fibrillation on hardware description language VHDL for use in the signal processing subsystem of a cardiorhythmogram based on FPGA was implemented. The findings suggest that the proposed model can be used in the construction of real-time heart rhythm control systems both for monitoring already diagnosed cardiovascular diseases, especially in intensive care wards, and for the prevention and early diagnosis of arrhythmias in individuals at high myocardial risk.