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Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/4204

Title: Methods of Nonlinear Dynamics for Heart Rate Variability Analysis
Other Titles: Методи на нелинейната динамика за анализ на вариабилността на сърдечната честота
Authors: Gospodinova, Evgeniya
Keywords: R/S Method
MFDFA Method
T-Test
ROC Analysis
Issue Date: 10-Jun-2022
Publisher: Institute of Mathematics and Informatics – Bulgarian Academy of Sciences
Citation: Gospodinova, E. (2022). Methods of Nonlinear Dynamics for Heart Rate Variability Analysis, Science Series "Innovative STEM Education", volume 04, ISSN: 2683-1333, Institute of Mathematics and Informatics – Bulgarian Academy of Sciences, 32-38. DOI: https://doi.org/10.55630/STEM.2022.0405
Series/Report no.: Science Series "Innovative STEM Education", volume 04;05
Abstract: The heart rate variability (HRV) analysis, based on the methods of nonlinear dynamics, can provide important information for the physiological interpretation of the functioning of the cardiovascular system and assess the risk of its pathology. The article presents methods for nonlinear analysis of HRV, united in the following groups: fractal, multifractal, graphical and informational. The application of the methods of nonlinear dynamics in the study of the information characteristics of HRV in order to distinguish healthy subjects from sick ones is an important topic from the point of view of the application of the information technologies in the field of non-invasive cardiology. After determining the values of the studied parameters with the developed software and for the distinction of the two studied groups of subjects (healthy controls and patients with arrhythmia) statistical analysis was applied. The statistical analysis was performed by t-test and receiver operating characteristic (ROC) analysis. ROC curves are constructed and the area under the curves is calculated, on the basis of which the quality of the studied methods is evaluated. The results reported in this study may be useful in classifying the states of electrocardiographic signals and serve as a landmark for comparing healthy individuals to individuals with cardiovascular disease. The high information content of the used nonlinear methods for HRV analysis opens perspectives for their future use in the diagnosis and prognosis of cardiovascular diseases.
URI: http://hdl.handle.net/10525/4204
ISSN: 2683-1333
Appears in Collections:STEM, vol.4, 2022

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