EstimatePSDSlope {RHRV} | R Documentation |
Estimate the slope of the Power Spectral Density (PSD).
Description
Estimate the slope of the Power Spectral Density (PSD) of the RR time series.
Usage
EstimatePSDSlope(HRVData, indexFreqAnalysis = length(HRVData$FreqAnalysis),
indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
regressionRange = NULL, doPlot = T, main = "PSD power law",
xlab = "Frequency (Hz)", ylab = "Spectrum", pch = NULL, log = "xy",
...)
Arguments
HRVData |
Data structure that stores the beats register and information related to it. |
indexFreqAnalysis |
An integer referencing the periodogram that will be used for estimating the spectral index. |
indexNonLinearAnalysis |
An integer referencing the structure that will store the resulting estimations. |
regressionRange |
Range of frequencies in which the regression will be performed. Default is c(1e-4, 1e-2) Hz. |
doPlot |
Plot the periodogram and the least-squares fit? |
main |
Title for the plot. |
xlab |
Title for the x axis. |
ylab |
Title for the y axis. |
pch |
Symbol for the plotting points. |
log |
A character string which contains "x" if the x axis is to be logarithmic, "y" if the y axis is to be logarithmic and "xy" or "yx" if both axes are to be logarithmic (default). |
... |
Other arguments for the plotting function. |
Details
The power spectrum of most physiological signals fulfils
S(f)=Cf^{-\beta}
(1/f spectrum). This function estimates
the \beta
exponent, which is usually referred to as the spectral
index.
Value
The EstimatePSDSlope returns the HRVData structure containing a PSDSlope field storing the spectral index and the proper Hurst exponent.
Note
It should be noted that the PSD must be estimated prior to the use of this function. We do not recommend the use of the AR spectrum when estimating the spectral index.
References
Voss, Andreas, et al. "Methods derived from nonlinear dynamics for analysing heart rate variability." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367.1887 (2009): 277-296.
Eke, A., Herman, P., Kocsis, L., & Kozak, L. R. (2002). Fractal characterization of complexity in temporal physiological signals. Physiological measurement, 23(1), R1.
See Also
Examples
## Not run:
data(HRVProcessedData)
# use other name for convenience
HRVData=HRVProcessedData
# Estimate the periodogram
HRVData=CreateFreqAnalysis(HRVData)
HRVData=CalculatePSD(HRVData,1,"pgram",doPlot = T,log="xy")
HRVData=CreateNonLinearAnalysis(HRVData)
HRVData=SetVerbose(HRVData,T)
HRVData=EstimatePSDSlope(HRVData,1,1,
regressionRange=c(5e-4,1e-2))
## End(Not run)