HEART RATE VARIABILITY ANALYSIS USING ARTIFICIAL NEURAL NETWORKS
DOI:
https://doi.org/10.26906/SUNZ.2024.1.109Keywords:
neuron, machine learning, heart rate variability, diagnosis of heart disease, RR intervalAbstract
The article provides a brief review and analysis of existing algorithms and software implementations of diagnostic systems for assessing heart rate variability, based on machine learning methods. The advantages of using an artificial neural network to classify types of electrocardiographic signals are presented, which improves the efficiency and quality of functional diagnostics of cardiac activity. To identify the most effective option for constructing neural network blocks for the hardware-software complex for analyzing heart rate variability, several options for implementing the construction of a neural network have been proposed. An analysis of methods and algorithms for morphological analysis of an electrocardiogram is carried out and the main stages of designing an artificial neural network as a classifier for pattern recognition such as RR intervals are given.Downloads
References
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