Modelo matemático para estudo da variabilidade da frequência cardíaca

Abstract

In recent years, the increase in the incidence of cardiovascular diseases in the world population has motivated the scientific community to seek new techniques or technological innovations to complement existing methods for assessing heart performance. Among them, stands out the analysis of the Heart Rate Variability (HRV) by electrocardiogram (ECG), an important non-invasive method for the detection of mild and moderate pathologies that are increasingly frequent in humans such as coronary diseases, arrhythmias, bradycardia and tachycardias, besides disturbances in the relationship between the sympathetic and parasympathetic nervous systems. This work introduces an innovation in a mathematical model based on Gaussian exponential modulations used to reproduce the ECG morphology of humans. This is the introduction of tachograms generated by an autoregressive stochastic process (AR), prior to the integration of the diferential equations of the model, capable of reproducing with better delity the HRV when compared with experimental data of healthy adults and adults with coronary artery disease (CAD). In order to validate the model, the simulated results are compared with experimental data using the discrete wavelet transform (DWT) power spectrum, Poincare plots and the detrended uctuation analysis (DFA). We verified that CAD does not change the morphology of ECG in resting state, but it has a significant in uence on HRV, and the proposed mathematical model absorbs and reproduces this behavior.