Effect of Calculation Algorithm on Heart Rate Variability by Chaos Theory

Abstract

Heart rate variability (HRV) is the physiological phenomenon of variation in the time interval between heartbeats. It is measured by the variation in the beat-tobeat interval. HRV parameters providing information about the scaling behavior or the complexity of the cardiac system were included. In addition, Chaos theory is a field of study in mathematics, with applications in several disciplines including physics, engineering, economics, biology, and philosophy. Chaos theory studies the behavior of dynamical systems that are highly sensitive to initial conditions, an effect which is popularly referred to as the butterfly effect. The goal was to investigate the influence of gender, age and day-night variation on these nonlinear HRV parameters. Numerical titration yielded similar information as other nonlinear HRV parameters do. However, it does not require long and cleaned data and therefore applicable on short (15 minutes) noisy time series. A higher nonlinear behavior was observed during the night while nonlinear heart rate fluctuations decline with increasing age. Our results support the involvement of the autonomic nervous system in the generation of nonlinear and complex heart rate dynamics. 

Implementation of Wireless Electrocardiogram Monitoring System

“…4 displays a Graphical User Interface (GUI) for visualising ECG signal. In addition, the calculation of heart rate was calculated using the peak of R-wave, in the term of the time period in between the R-peak to R-peak, identified as R-R interval [13], [14]. The calculation of heart rate in beat per minute can be determined by (2).…”

A non-invasive approach for the diagnosis of Type 2 diabetes using HRV parameters

“…A different approach used is based on the Principal Component Analysis (PCA) (Egorova et al, 2014). The results of HRV analysis based on these methods have been used to diagnose various types of medical conditions (Deepak et al, 2013;Dong et al, 2014;Ulanovsky et al, 2014;Tonello et al, 2014;ChuDuc et al, 2013). Many of the previous investigations have established the fact that there is a certain relation between diabetic conditions of a subject and the HRV parameters.…”