A new method based on fractal variance function for analysis and quantification of sympathetic and vagal activity in variability of R–R time series in ECG signals

It is known that R–R time series calculated from a recorded ECG, are strongly correlated to sympathetic and vagal regulation of the sinus pacemaker activity. In human physiology it is a crucial question to estimate such components with accuracy. Fourier analysis dominates still to day the data analysis efforts of such data ignoring that FFT is valid under some crucial restrictions that results largely violated in R–R time series data as linearity and stationarity. In order to go over such approach, we introduce a new method, called CZF. It is based on variogram analysis. It is aimed from a profound link with Recurrence Quantification Analysis that is a basic tool for investigation of non linear and non stationary time series. Therefore, a relevant feature of the method is that it finally may be applied also in cases of non linear and non stationary time series analysis. In addition, the method enables also to analyze the fractal variance function, the Generalized Fractal Dimension and, finally, the relative probability density function of the data. The CZF gives very satisfactory results. In the present paper it has been applied to direct experimental cases of normal subjects, patients with hypertension before and after therapy and in children under some different conditions of experimentation.