Abstract
BACKGROUND: Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. Linear parameters, Power spectral indice (LF/HF) is calculated with nonlinear indices Poincare plot geometry(SD1,SD2), Approximate Entropy (ApEn), Largest Lyapunov Exponent (LLE) and Detrended Fluctuation Analysis(DFA). The results show that, with aging the heart rate variability decreases. In this work, the ranges of all linear and nonlinear parameters for four age group normal subjects are presented with an accuracy of more than 89%. As a pre-analysis step, the HRV data is tested for nonlinearity using surrogate data analysis and the results exhibited a significant difference in the ApEn, LLE, SD1/SD2 and DFA parameters of the actual data and the surrogate data. METHODS: The heart rate is analyzed using the various time domain parameters, frequency domain parameter and nonlinear parameters like Poincare geometry, ApEn, LLE and DFA. RESULTS: In this work, the different linear and nonlinear parameters evaluated show a particular range for various cardiac abnormalities. And the results of these were subjected to 't' test with more than 89% confidence interval giving excellent 'p' values in all cases. CONCLUSIONS: Heart rate variability (HRV) signal can be used as a reliable indicator of state of the heart. It becomes less random with the aging(less chaotic). This is evaluated by using various time domain, frequency domain and nonlinear parameters like SD1/SD2, ApEn, LLE alphas and alphal. Different ranges of non-linear parameters for various age groups are presented with 'p' value < or = 0.12.