HEART RATE VARIABILITY (HRV) SIGNAL PROCESSING BY USING WAVELET BASED MULTIFRACTAL ANALYSIS

摘要

Fractal geometry and wavelets are a new and promising approach to analyze and characterize non-stationary signals such as ECG, EEG and stock price etc. Heart Rate Variability Signals, derived from an ECG signal, arc strongly related to the activity of the autonomous nervous system(ANS). HRV is usually investigated as RR variability since the R wave is far easier to detect due to its peaked shape. The classical methods based on autocorrelation, thresholds or derivatives, time domain methods and frequency domain methods give a coarse quantification of the variability, without distinguishing between short-term and long-term fluctuations. In this paper, we propose a new wavelet based method to analyze Heart Rate Variability (HRV) signals. The fractal dimension of the RR series can be calculated by using wavelets, time being here irrelevant. Another measure, Multifractal Spectrum is computed with the help of a scaling exponent. Using this strategy, we found that the peak of the multifractal spectrum shifted to higher dimensions and demonstrated increased complexity and an increasing amount of "noise" for ANS regulations of HRV signals during the tilt interval.