---
title: "RHRV Quick Start Tutorial"
author: "RHRV Team"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{RHRV-quickstart}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
In this vignette, a brief description of the **RHRV** package is presented.
Due to the large collection of features that **RHRV** offers, we
shall only refer to the most important functionality to
perform a basic Heart Rate Variability (HRV) analysis. The interested
reader is referred to the
[free tutorial](http://rhrv.r-forge.r-project.org/tutorial/tutorial.pdf) for
further information.
## Installation
This vignette assumes that the user has some basic
knowledge of the **R** environment. If this is not your case, you can find a nice
[introduction to R](https://cran.r-project.org/) in the **R** project homepage.
The **R** project
homepage also provides an
[R Installation and Administration guide](https://cran.r-project.org/). Once you
have download and installed R, you can install **RHRV** by typing:
```{r install, eval = FALSE}
install.packages("RHRV", dependencies = TRUE)
```
You can also install it by downloading it from the [CRAN](https://cran.r-project.org/package=RHRV). Once the
download has finished, open **R**, move to the directory where you have download
it (by using the **R** command `setwd` and type:
```{r installDownloaded, eval = FALSE}
setwd(sourceDirectory)
install.packages("RHRV_XXX",repos = NULL)
```
Here, XXX is the version number of the library. To start using the library, you
should load it by using:
```{r library, message=FALSE, warning=FALSE}
library('RHRV')
```
## A 15-minutes guide to RHRV
We propose the following basic program flow to perform a basic HRV
analysis using the **RHRV** package:
* Load heart beat positions. For the sake of simplicity, in this document we
will focus in ASCII files.
* Build the instantaneous Heart Rate (HR) series and filter it to eliminate
spurious points.
* Plot the instantaneous HR series.
* Interpolate the instantaneous HR series to obtain a HR series
with equally spaced values.
* Plot the interpolated HR series.
* Perform the desired analysis. The user can perform time-domain analysis,
frequency-domain analysis and/or nonlinear analysis. Since nonlinear analysis
techniques make use of advanced concepts, this vignette focuses in
time and frequency domain analysis.
* Plot the results of the analysis that has been performed.
We shall deal with each of these points in the following sections. All the examples
of this vignette will use the example beats file "example.beats" that may be
downloaded from [here](http://rhrv.r-forge.r-project.org/tutorial/example.beats).
Additionally, the data from this file has been included in RHRV. The user can
access this data executing:
```{r accessingData, eval=FALSE}
# HRVData structure containing the heart beats
data("HRVData")
# HRVData structure storing the results of processing the
# heart beats: the beats have been filtered, interpolated, ...
data("HRVProcessedData")
```
The example file is an ASCII file that contains the beats positions obtained
from a 2 hours electrocardiogram (ECG), with one beat position per row. The
subject of the ECG is a patient suffering from paraplegia and hypertension (systolic
blood pressure above 200 mm-Hg). During the recording, he is supplied with
prostaglandin E1 (a vasodilator that is rarely employed) and systolic blood
pressure fell to 100 mm-Hg for over an hour. Then, the blood pressure increased
slowly up to approximately 150 mm-Hg.
## Preprocessing the Heart Rate series
### Load heart beat positions
**RHRV** uses a custom data structure
called `HRVData` to store all HRV information related to the signal
being analysed. The following Figure summarizes the
most important fields in the
`HRVData` structure. `HRVData` is implemented as a list object
in **R** language. This list contains all the information corresponding
to the signal to be analysed, some parameters generated
by the pre-processing functions and the HRV analysis results. It must be noted
that, since the `HRVData` structure is a list, each of its fields can be
accessed using the `$` operator of the **R** language.
```{r includeFigure, echo=FALSE, fig.align='center',fig.cap="The most important fields stored in the *HRVData* structure"}
knitr::include_graphics("figures/basicHRVData.png")
```
```{r creation, eval=TRUE}
hrv.data = CreateHRVData()
hrv.data = SetVerbose(hrv.data, TRUE )
```
After creating the empty `HRVData` structure the next step should be
loading the signal that we want to analyse into this structure. **RHRV** imports
data files containing heart beats positions. Supported formats include ASCII
(`LoadBeatAscii` function), EDF (`LoadBeatEDFPlus`), Polar
(`LoadBeatPolar`), Suunto (`LoadBeatSuunto`) and WFDB data files
(`LoadBeatWFDB`). For the sake of simplicity, we will
focus in ASCII files containing one heart beat occurrence time per line. We also assume
that the beat occurrence time is specified in seconds. For example, let's try to load the
"example.beats" file, whose first lines are shown below. Each line denotes
the occurrence time of each heartbeat.
````
0
0.3280001
0.7159996
1.124
1.5
1.88
...
````
In order to load this file, we may write:
```{r loading}
hrv.data = LoadBeatAscii(hrv.data, "example.beats",
RecordPath = "beatsFolder")
```
The console information is only displayed if the verbose mode is on. The
`Scale` parameter is related to the time units of the file. 1 denotes seconds,
0.1 tenth of seconds and so on. The `Date` and `Time`
parameters specify when the file was recorded. The `RecordPath` can be omitted
if the `RecordName` is in the working directory.
Further information about this function and other input formats may be found
in the [online tutorial](http://rhrv.r-forge.r-project.org/tutorial/tutorial.pdf).
### Calculating HR and filtering
To compute the HRV time series the `BuildNIHR` function can be used
(*Build Non Interpolated Heart Rate*). This function constructs both the RR and
instantaneous heart rate (HR) series. We will refer to the instantaneous Heart
Rate as the niHR (non interpolated Heart Rate) series. Both series are stored in
the `HRVData` structure.
```{r derivating}
hrv.data = BuildNIHR(hrv.data)
```
A Filtering operation must be carried out in order to eliminate outliers or
spurious points present in the niHR time series with
unacceptable physiological values. Outliers present in the series originate both
from detecting an artefact as a heartbeat (RR interval too short) or not
detecting a heartbeat (RR interval too large). The
outliers removal may be both manual or automatic. In this quick introduction,
we will use the automatic removal. The automatic removal of spurious points
can be performed by the `FilterNIHR` function. The `FilterNIHR`
function also eliminates points with unacceptable physiological values.
```{r filtering}
hrv.data = FilterNIHR(hrv.data)
```
### Interpolating
In order to be able to perform spectral analysis
in the frequency domain, a uniformly sampled HR series is required. It
may be constructed from the niHR series by using the
`InterpolateNIHR` function, which uses linear (default) or spline
interpolation. The frequency of interpolation may be specified.
4 Hz (the default value) is enough for most applications.
```{r interpolating}
# Note that it is not necessary to specify freqhr since it matches with
# the default value: 4 Hz
hrv.data = InterpolateNIHR(hrv.data, freqhr = 4)
```
### Plotting
Before applying the different analysis techniques that RHRV provides, it
is usually interesting to plot the time series with which we are working. The
`PlotNIHR` function permits the graphical representation of the
niHR series whereas the `PlotHR` function permits to
graphically represent the interpolated HR time series. The two plots are usually
very similar. For example, we could type:
```{r plottingHR, fig.align="center",fig.width=5,fig.height=4}
PlotNIHR(hrv.data, main = "niHR")
```
As seen in the previous figure, the
patient initially had a heart rate of approximately 160 beats per minute.
Approximately half an hour into record the prostaglandina E1 was provided,
resulting in a drop in heart rate to about 130 beats per minute during about 40
minutes, followed by a slight increase in heart rate.
## Analysing the Heart Rate series
### Time-domain analysis techniques\label{sec:quickTime}
The simplest way of performing a HRV analysis in **RHRV** is using the time
analysis techniques
provided by the `CreateTimeAnalysis` function. This function computes
the most widely used time-domain parameters and stores them in the
`HRVData` structure. The most interesting parameter that the user may
specify is the width of the window that
will be used to analyse short segments from the RR time series (`size`
parameter, in seconds). Concretely, several statistics will be computed for
each window. By studying how these statistics
evolve through the recording, a set of time parameters will be computed (For
example, the `SDANN` and `SDNNIDX` parameters). Other important argument that
can be tuned is the interval width of the bins that will be used to compute the histogram
(`interval` parameter). As an alternative to the `interval` parameter, the user may use the
`numofbins` parameter to specify the number of bins in the histogram. A
typical value for the `size` parameter is 300 seconds (which is also the
default value), whereas that a typical value for the `interval` is about
7.8 milliseconds (also default value).
```{r timeAnalysis, eval=FALSE}
hrv.data = CreateTimeAnalysis(hrv.data, size = 300,
interval = 7.8125)
```
If the verbose mode is on, the program will display the results of the
calculations on the screen. Otherwise, the user must access the "raw" data
using the `$` operator of the **R** language.
Finally, we show a complete example for performing a basic time-domain
analysis. The console output is also shown. It should be noted that it is not
necessary to perform the interpolation process before applying
the time-domain techniques since these parameters are calculated directly from
the non interpolated RR-time series.
```{r completeTimeAnalysis}
hrv.data = CreateHRVData()
hrv.data = SetVerbose(hrv.data,FALSE)
hrv.data = LoadBeatAscii(hrv.data,"example.beats","beatsFolder")
hrv.data = BuildNIHR(hrv.data)
hrv.data = FilterNIHR(hrv.data)
hrv.data = SetVerbose(hrv.data,TRUE)
hrv.data = CreateTimeAnalysis(hrv.data,size=300,interval = 7.8125)
# We can access "raw" data... let's print separately, the SDNN
# parameter
cat("The SDNN has a value of ",hrv.data$TimeAnalysis[[1]]$SDNN," msec.\n")
```
### Frequency-domain analysis techniques
A major part of the functionality of the **RHRV** package is dedicated to the
spectral analysis of HR signals. Before performing the frequency
analysis, a data analysis structure must be created. Such structure shall store
the information extracted from a variability analysis of the HR signal as
a member of the `FreqAnalysis` list, under the `HRVData`
structure. Each analysis structure created is identified by a unique number (in
order of creation). To create such an analysis structure, the
`CreateFreqAnalysis` function is used.
```{r creatingFreq}
hrv.data = CreateFreqAnalysis(hrv.data)
```
Notice that, if verbose mode is on, the `CreateFreqAnalysis` function
informs us about the number of frequency analysis structures that have been
created. In order to select a particular spectral analysis, we will use the
`indexFreqAnalysis` parameter in the frequency analysis functions.
The most important function to perform spectral HRV analysis is the
`CalculatePowerBand` function. The `CalculatePowerBand` function
computes the spectrogram of the HR series in the *ULF* (Ultra
Low Frequency), *VLF* (Very Low Frequency), *LF* (Low Frequency) and *HF*
(High Frequency) bands using the Short Time Fourier Transform (STFT) or wavelets.
Boundaries of the bands may be chosen by the user. If boundaries are not specified,
default values are used: *ULF*, [0, 0.03] Hz;
*VLF*, [0.03, 0.05] Hz; *LF*, [0.05, 0.15] Hz;
*HF*, [0.15, 0.4] Hz. The type of analysis can be selected by
the user by specifying the `type` parameter of the `CalculatePowerBand`
function. The possible options are either `"fourier"` or `"wavelet"`.
Because of the backwards compatibility, the default value for this parameter
is `"fourier"`.
#### Fourier
When using the STFT to compute the spectrogram
using the `CalculatePowerBand` function, the user may specify the
following parameters related with the STFT:
* `Size`: the size of window for calculating the spectrogram measured in
seconds. The **RHRV** package employs a Hamming window to perform the
STFT.
* `Shift`: the displacement of window for calculating the spectrogram measured
in seconds.
* `Sizesp`: the number of points for calculating each window of the STFT.
If the user does not specify it, the program selects a proper length for the
calculations (it selects `sizesp` so that `sizesp`$=2^m$, for some
$m \in \mathbb{N}$).
When using `CalculatePowerBand`, the `indexFreqAnalysis`
parameter (in order to indicate which spectral analysis we are working with)
and the boundaries of the frequency bands may also be specified.
As an example, let's perform a frequency analysis in the typical HRV
spectral bands based on the STFT . We may select 300 s (5 minutes) and 30
s as window size and displacement values because these are typical values when
performing HRV spectral analysis. We let the program choose the value of the
zero-padding. Thus, we may write:
```{r STFTanalysis}
hrv.data = CreateHRVData( )
hrv.data = SetVerbose(hrv.data,FALSE)
hrv.data = LoadBeatAscii(hrv.data,"example.beats","beatsFolder")
hrv.data = BuildNIHR(hrv.data)
hrv.data = FilterNIHR(hrv.data)
hrv.data = InterpolateNIHR (hrv.data, freqhr = 4)
hrv.data = CreateFreqAnalysis(hrv.data)
hrv.data = SetVerbose(hrv.data,TRUE)
# Note that it is not necessary to write the boundaries
# for the frequency bands, since they match
# the default values
hrv.data =
CalculatePowerBand(hrv.data , indexFreqAnalysis = 1,
size = 300, shift = 30, type = "fourier",
ULFmin = 0, ULFmax = 0.03, VLFmin = 0.03, VLFmax = 0.05,
LFmin = 0.05, LFmax = 0.15, HFmin = 0.15, HFmax = 0.4 )
```
Alternatively, since most values of the arguments match its default values
we could have written:
```{r STFTanalysis2, eval= FALSE}
hrv.data = CalculatePowerBand(hrv.data, indexFreqAnalysis= 1,
size = 300, shift = 30)
```
#### Wavelets
When using Wavelet analysis with the `CalculatePowerBand` function, the user may specify:
* `wavelet`: mother wavelet used to calculate the spectrogram. Some
of the most widely used Wavelets are available: Haar (`"haar"`), extremal phase
(`"d4"`, `"d6"`, `"d8"` and `"d16"`) and the least asymmetric Daubechies (`"la8"`,
`"la16"` and `"la20"`) and the best localized Daubechies (`"bl14"` and `"bl20"`)
Wavelets among others. The default value is `"d4"`. The name of the wavelet
specifies the `family` (the family determines the shape of the Wavelet and
its properties) and the length of the wavelet. For example, `"la8"` belongs to
the Least Asymmetric family and has a length of 8 samples. We may give a simple
advice for wavelet selection based on the wavelet's length: shorter wavelets
usually have better temporal resolution, but worse frequency resolution. On the
other hand, longer wavelets usually have worse temporal resolution, but they
provide better frequency resolution. Better temporal resolution means that we
can study shorter time intervals. On the other hand, a better frequency
resolution means better *frequency discrimination*. That is, shorter wavelets
will tend to fail when discriminating close frequencies.
* `bandtolerance`: maximum error allowed when the Wavelet-based
analysis is performed. It can be specified as an absolute or a relative error
depending on the `relative` parameter value. Default value is 0.01.
* `relative`: logic value specifying which type of band tolerance
shall be used: relative (in percentage) or absolute (default value). For the sake
of simplicity, in this document we will use the absolute band tolerance.
Let's analyse the same frequency bands as before but using the
wavelet-algorithm. For the sake of simplicity, we will use an absolute
tolerance of 0.01 Hz. We may select the least asymmetric Daubechies of width
8 (`"la8"`) as wavelet, since it provides a good compromise between frequency and time
resolution. Thus, we may write:
```{r waveletAnalysis}
hrv.data = CreateHRVData( )
hrv.data = SetVerbose(hrv.data,FALSE)
hrv.data = LoadBeatAscii(hrv.data,"example.beats","beatsFolder")
hrv.data = BuildNIHR(hrv.data)
hrv.data = FilterNIHR(hrv.data)
hrv.data = InterpolateNIHR (hrv.data, freqhr = 4)
hrv.data = CreateFreqAnalysis(hrv.data)
hrv.data = SetVerbose(hrv.data,TRUE)
# Note that it is not necessary to write the boundaries
# for the frequency bands, since they match the default values
hrv.data =
CalculatePowerBand( hrv.data , indexFreqAnalysis = 1,
type = "wavelet", wavelet = "la8",
bandtolerance = 0.01, relative = FALSE,
ULFmin = 0, ULFmax = 0.03, VLFmin = 0.03, VLFmax = 0.05,
LFmin = 0.05, LFmax = 0.15, HFmin = 0.15, HFmax = 0.4 )
```
#### Creating several analyses
In the previous examples we have used just one frequency analysis to
illustrate the basic use of `CalculatePowerBand`. However, it is
possible to create and use the same `HRVData` for performing several spectral
analysis. When we do this, we use the parameter `indexFreqAnalysis` to
indicate which spectral analysis we are working with. For example, we could perform both
Fourier and wavelet based analysis:
```{r echo=FALSE}
hrv.data = CreateHRVData( )
hrv.data = SetVerbose(hrv.data, FALSE)
hrv.data = LoadBeatAscii(hrv.data,"example.beats","beatsFolder")
hrv.data = BuildNIHR(hrv.data)
hrv.data = FilterNIHR(hrv.data)
hrv.data = InterpolateNIHR(hrv.data, freqhr = 4)
```
```{r bothAnalysis,message=FALSE}
# ...
# create structure, load beats, filter and interpolate
hrv.data = CreateFreqAnalysis(hrv.data)
hrv.data = SetVerbose(hrv.data, FALSE)
# use freqAnalysis number 1 for perfoming
# Fourier analysis. This time, we do not
# write the band's boundaries
hrv.data = CalculatePowerBand(hrv.data , indexFreqAnalysis = 1,
size = 300, shift = 30, sizesp = 2048,
type = "fourier")
# use freqAnalysis number 2 for perfoming
# wavelet analysis. Note the indexFreqAnalysis = 2!!!
hrv.data = CreateFreqAnalysis(hrv.data)
hrv.data = CalculatePowerBand(hrv.data, indexFreqAnalysis= 2,
type = "wavelet", wavelet = "la8",
bandtolerance = 0.01, relative = FALSE)
```
#### Plotting
**RHRV** also includes plotting utilities for representing
the spectrogram of each frequency band: the `PlotPowerBand` function.
`PlotPowerBand` receives as inputs the `HRVData`
structure and the index of the frequency analysis that the user wants to
plot (`indexFreqAnalysis` argument). Optionally, the user can specify
additional parameters for modifying the plots (whether to use or not normalized
plots, specify the y-axis, etc.). For the sake of simplicity we will only use
the `ymax` parameter (for specifying the maximum y-axis of the power
bands plot) and the `ymaxratio` parameter
(for specifying the maximum y-axis in the *LF/HF* plot).
If we want to plot the power bands computed in the previous example, we may
use:
```{r plottingFreqFourier,fig.align="center", fig.width=6,fig.height=6}
# Plotting Fourier analysis
PlotPowerBand(hrv.data, indexFreqAnalysis = 1, ymax = 200, ymaxratio = 1.7)
```
```{r plottingFreqWavelet,fig.align="center", fig.width=6, fig.height=6}
# Plotting wavelet analysis
PlotPowerBand(hrv.data, indexFreqAnalysis = 2, ymax = 700, ymaxratio = 50)
```
#### A brief comparison: Wavelets Vs. Fourier
The previous Figures illustrate some of the most important differences between
Fourier and wavelet-based analysis. The most important differences may be
summarized as follows:
* The power range is not the same when using Fourier than when using
wavelets due
to the windowing used in both techniques. Thus, we
should avoid direct comparisons between the numerical results obtained with
Fourier with those obtained using wavelets.
* The Fourier's power spectrum is smoother than the wavelet's power
spectrum. This is a consequence of the higher temporal resolution that the
wavelet-based analysis provides. We could try to increase Fourier's
frequency resolution by decreasing the window' size used in the analysis. The
shorter window we use, the sharper spectrum we get. Similarly, we can
increase/decrease temporal resolution using shorter/larger wavelets when
performing
wavelet-based analysis.
* The power spectrum obtained from the Fourier-based analysis has a smaller
number of samples than the original signal as a consequence of the use of
windows. Conversely, the power spectrum obtained from the wavelet-based
analysis has the same number of samples as the original RR time series.