Mathematical Technologies for Modeling of Cardiological Data: Heart Rate Variability

Proceedings of ‏The 3rd International Conference on Applied Research in Engineering, Science and Technology

Year: 2020

DOI:

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Mathematical Technologies for Modeling of Cardiological Data: Heart Rate Variability

Galya Nikolova Georgieva-Tsaneva, Evgeniya Peneva Gospodinova

 

ABSTRACT: 

Cardiovascular diseases are widespread worldwide, causing a decline in the quality of life of the population and  many deaths each year. For these reasons, the study and modeling of cardiac data is a challenge for the research community. Heart rate variability derived from electrocardiographic and holter recordings is a time series formed by the intervals between successive heart beats. This paper presents an overview of mathematical technologies for heart rate variability modeling. The most effective and the most used mathematical models in the scientific literature are presented of heart rate variability: the Zeeman model, Gaussian functions model, mathematical model based on the Integral Pulse Frequency Modulation and others. A mathematical algorithm for heart rate variability modeling using 2 Gaussian functions was analyzed. The use of Wavelet analysis technology in the processing of cardiac data and in their mathematical modeling is presented. Various wavelet bases used in  heart rate variability modeling have been investigated. The results presented show the impact of these wavelet  bases on the proposed mathematical model. The effect of the size of the created time series on the program execution time of the algorithm was also investigated. The mathematical model presented can be used to model realistic cardiac intervals and be used in training future physicians, as well as to conduct research on new algorithms for cardiac data processing and analysis.

Keywords: cardiological data, heart rate variability, modeling, Wavelet analysis, Gaussian functions.