The Poincare plot is a graphical representation of the dependance between successive RR intervals obtained by plotting the \(RR_{j+\tau}\) as a function of \(RR_j\). This dependance is often quantified by fitting an ellipse to the plot. In this way, two parameters are obtained: \(SD_1\) and \(SD_2\). \(SD_1\) characterizes short-term variability whereas that \(SD_2\) characterizes long-term variability.
PoincarePlot(
HRVData,
indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
timeLag = 1,
confidenceEstimation = FALSE,
confidence = 0.95,
doPlot = FALSE,
main = "Poincare plot",
xlab = "RR[n]",
ylab = paste0("RR[n+", timeLag, "]"),
pch = 1,
cex = 0.3,
type = "p",
xlim = NULL,
ylim = NULL,
...
)
A HRVData structure containing a PoincarePlot field storing the \(SD_1\) and \(SD_2\) parameters. The PoincarePlot field is stored under the NonLinearAnalysis list.
Data structure that stores the beats register and information related to it
Reference to the data structure that will contain the nonlinear analysis
Integer denoting the number of time steps that will be use to construct the dependance relation: \(RR_{j+timeLag}\) as a function of \(RR_j\).
Logical value. If TRUE, the covariance matrix is used for fitting the ellipse and computing the \(SD_1\) and \(SD_2\) parameters (see details). Default: FALSE.
The confidence used for plotting the confidence ellipse.
Logical value. If TRUE (default), the PoincarePlot is shown.
An overall title for the Poincare plot.
A title for the x axis.
A title for the y axis.
Plotting character (symbol to use).
Character (or symbol) expansion.
What type of plot should be drawn. See plot.default
.
x coordinates range. If not specified, a proper x range is selected.
y coordinates range. If not specified, a proper y range is selected.
Additional parameters for the Poincare plot figure.
In the HRV literature, when timeLag = 1, the \(SD_1\) and \(SD_2\) parameters are computed using time domain measures. This is the default approach in this function if timeLag=1. This function also allows the user to fit a ellipse by computing the covariance matrix of (\(RR_{j}\),\(RR_{j+\tau}\)) (by setting confidenceEstimation = TRUE). In most cases, both approaches yield similar results.
if (FALSE) {
data(HRVProcessedData)
# rename for convenience
hd = HRVProcessedData
hd = CreateNonLinearAnalysis(hd)
hd = PoincarePlot(hd, doPlot = T)
}
Run the code above in your browser using DataLab