Package 'RHRV'

Description

RHRV offers functions for performing power spectral analysis of heart rate data. We will use this package for the study of several diseases, such as obstructive sleep apnoea or chronic obstructive pulmonary disease.

Details

This is a package for developing heart rate variability studies of ECG records. Data are read from an ascii file containing a column with beat positions in seconds. A function is included in order to build this file from an ECG record in WFDB format (visit the site http://www.physionet.org for more information).

Note

An example including all the necessary steps to obtain and to
analyze by episodes the power bands of a wfdb register is
giving below:

##Reading a wfdb register and storing into a data structure:
md = CreateHRVData(Verbose = TRUE)
md = LoadBeatWFDB(md, RecordName = "register_name",
RecordPath = "register_path")

##Loading information of episodes of apnea:
md = LoadApneaWFDB(md, RecordName = "register_name",
RecordPath = "register_path", Tag = "APN")

##Generating new episodes before and after previous episodes of
apnea:
md = GenerateEpisodes(md, NewBegFrom = "Beg", NewEndFrom = "Beg",
DispBeg = -600, DispEnd = -120, OldTag = "APN",
NewTag = "PREV_APN")
md = GenerateEpisodes(md, NewBegFrom = "End", NewEndFrom = "End",
DispBeg = 120, DispEnd = 600, OldTag = "APN",
NewTag = "POST_APN")

##Calculating heart rate signal:
md = BuildNIHR(md)

##Filtering heart rate signal:
md = FilterNIHR(md)

##Interpolating heart rate signal:
md = InterpolateNIHR(md)

##Calculating spectrogram and power per band:
md = CreateFreqAnalysis(md)
md = CalculatePowerBand(md, indexFreqAnalysis = 1, size = 120,
shift = 10, sizesp = 1024)

##Plotting power per band, including episodes information:
PlotPowerBand(md, indexFreqAnalysis = 1, hr = TRUE, ymax = 2400000,
ymaxratio = 3, Tag = "all")

##Splitting power per band using episodes before and after
episodes of apnea:
PrevAPN = SplitPowerBandByEpisodes(md, indexFreqAnalysis = 1,
Tag = "PREV_APN")
PostAPN = SplitPowerBandByEpisodes(md, indexFreqAnalysis = 1,
Tag = "POST_APN")

##Performing Student's t-test:
result = t.test(PrevAPN$InEpisodes$ULF, PostAPN$InEpisodes$ULF)
print(result)

Author(s)

A. Mendez, L. Rodriguez, A. Otero, C.A. Garcia, X. Vila, M. Lado

Maintainer: Leandro Rodriguez-Linares <[email protected]>

References

L. Rodriguez-Linares, L., A.J. Mendez, M.J. Lado, D.N. Olivieri, X.A. Vila, and I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103(1):39-50, july 2011.

Description

Adds information of episodes manually, or annotated physiological events, and stores it into the data structure containing the beat positions

Usage

AddEpisodes(HRVData, InitTimes, Tags, Durations, Values, verbose=NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register and new episodes information

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

Analyzes Heart Rate allowing to evaluate the application of a desired function inside and outside episodes

Usage

AnalyzeHRbyEpisodes(HRVData, Tag="", func, ..., verbose=NULL)

Arguments

Value

Returns a list with two objects, that is, the values of the application of the selected function inside and outside episodes

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

See Also

SplitHRbyEpisodes for splitting in two parts Heart Rate Data using an specific episode type

Description

Analyzes the ULF, VLF, LF and HF bands from a given indexFreqAnalysis allowing to evaluate the application of a desired function inside and outside each episode.

Usage

AnalyzePowerBandsByEpisodes(
  HRVData,
  indexFreqAnalysis = length(HRVData$FreqAnalysis),
  Tag = "",
  verbose = NULL,
  func,
  ...
)

Arguments

Value

Returns a list with two objects, that is, the values of the application of the selected function inside ("resultIn") and outside ("resultOut") episodes in the given indexFreqAnalysis. Each of these list has another set of lists: the "ULF", "VLF", "LF" and "HF" lists.

Examples

## Not run: 
hrv.data = CreateHRVData()
hrv.data = SetVerbose(hrv.data, TRUE)
hrv.data = LoadBeat(hrv.data, fileType = "WFDB", "a03", RecordPath ="beatsFolder/", 
                    annotator = "qrs")
                    hrv.data = LoadApneaWFDB(hrv.data, RecordName="a03",Tag="Apnea",
                                             RecordPath="beatsFolder/")
hrv.data = BuildNIHR(hrv.data)
hrv.data = InterpolateNIHR (hrv.data, freqhr = 4)
hrv.data = CreateFreqAnalysis(hrv.data)
hrv.data = CalculatePowerBand( hrv.data , indexFreqAnalysis= 1,
                               type = "wavelet", wavelet = "la8",
                                bandtolerance = 0.01, relative = FALSE)
results = AnalyzePowerBandsByEpisodes(hrv.data,indexFreqAnalysis=1,
                                       Tag="Apnea",func=mean)
## End(Not run)

Description

WARNING: deprecated function. The Integral correlation is calculated for every vector of the m-dimensional space, and then the average of all these values is calculated

Usage

AvgIntegralCorrelation(HRVData, Data, m, tau, r)

Arguments

Value

Returns the value of the average of IntegralCorrelations

Note

This function is used in the CalculateApEn function, which is deprecated. We suggest the use of the CalculateSampleEntropy function instead of CalculateApEn.

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

See Also

IntegralCorrelation

Description

The instantaneous heart rate can be defined as the inverse of the time separation between two consecutive heart beats. Once the beats have been identified, and since the only valid values contributing to the heart rate signal are the corresponding to normal beats preceded by other normal beats, a further operation should be performed for the calculation of the instantaneous heart rate.

Usage

BuildNIHR(HRVData, verbose=NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register and now associated heart rate instantaneous values also

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

This function builds the Takens' vectors of the Non Interpolated RR intervals. The set of Takens' vectors is the result of embedding the time series in a m-dimensional space. That is, the $n^{th}$ Takens' vector is defined as

$T[n]=\{niRR[n], niRR[n+ timeLag],..., niRR[n+m*timeLag]\}.$

Taken's theorem states that we can then reconstruct an equivalent dynamical system to the original one (the dynamical system that generated the observed time series) by using the Takens' vectors.

Usage

BuildTakens(HRVData, embeddingDim, timeLag)

Arguments

Value

A matrix containing the Takens' vectors (one per row).

Note

This function is based on the buildTakens function from the nonlinearTseries package.

References

H. Kantz and T. Schreiber: Nonlinear Time series Analysis (Cambridge university press)

Description

WARNING: deprecated function. In order to calculate de Fractal Dimension and Approximate Entropy (or others properties of the data) a representation of the data in a space m-dimensional is needed

Usage

BuildTakensVector(HRVData, Data, m, tau)

Arguments

Value

Returns a matrix with the Expanded Data with N-(m-1)*tau rows (N is the length of the Data to be analyzed) and m columns

Note

This function is deprecated. Please use BuildTakens instead.

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

WARNING: deprecated function. Calculates Approximate Entropy as indicated by Pincus

Usage

CalculateApEn(HRVData,
              indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
              m = 2, tau = 1, 
              r = 0.2, N = 1000, verbose=NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register and now associated heart rate instantaneous values also, including the value of the Approximate Entropy

Note

This function is deprecated. We suggest the use of the CalculateSampleEntropy function instead, which is faster.

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011) S. M. Pincus, "Approximate entropy as a measure of system complexity," Mathematics 88, 2297-2301 (1991)

See Also

BuildTakensVector for expand data
IntegralCorrelation for correlation calculations
AvgIntegralCorrelation for averaging correlation calculations

Description

Functions for estimating the correlation sum and the correlation dimension of the RR time series using phase-space reconstruction

Usage

CalculateCorrDim(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  minEmbeddingDim = NULL,
  maxEmbeddingDim = NULL,
  timeLag = NULL,
  minRadius,
  maxRadius,
  pointsRadius = 20,
  theilerWindow = 100,
  corrOrder = 2,
  doPlot = TRUE
)

EstimateCorrDim(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  regressionRange = NULL,
  useEmbeddings = NULL,
  doPlot = TRUE
)

PlotCorrDim(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  ...
)

Arguments

Details

The correlation dimension is the most common measure of the fractal dimensionality of a geometrical object embedded in a phase space. In order to estimate the correlation dimension, the correlation sum is defined over the points from the phase space:

$C(r) = \{(number\;of\;points\;(x_i,x_j)\;verifying\;that\;distance\;(x_i,x_j)<r\})/N^2$

However, this estimator is biased when the pairs in the sum are not statistically independent. For example, Taken's vectors that are close in time, are usually close in the phase space due to the non-zero autocorrelation of the original time series. This is solved by using the so-called Theiler window: two Takens' vectors must be separated by, at least, the time steps specified with this window in order to be considered neighbours. By using a Theiler window, we exclude temporally correlated vectors from our estimations.

The correlation dimension is estimated using the slope obtained by performing a linear regression of $\log10(C(r))\;Vs.\;\log10(r)$. Since this dimension is supposed to be an invariant of the system, it should not depend on the dimension of the Taken's vectors used to estimate it. Thus, the user should plot $\log10(C(r))\;Vs.\;\log10(r)$ for several embedding dimensions when looking for the correlation dimension and, if for some range $\log10(C(r))$ shows a similar linear behaviour in different embedding dimensions (i.e. parallel slopes), these slopes are an estimate of the correlation dimension. The estimate routine allows the user to get always an estimate of the correlation dimension, but the user must check that there is a linear region in the correlation sum over different dimensions. If such a region does not exist, the estimation should be discarded.

Note that the correlation sum C(r) may be interpreted as: $C(r) = <p(r)>,$ that is: the mean probability of finding a neighbour in a ball of radius r surrounding a point in the phase space. Thus, it is possible to define a generalization of the correlation dimension by writing:

$C_q(r) = <p(r)^{(q-1)}>$

Note that the correlation sum

$C(r) = C_2(r)$

It is possible to determine generalized dimensions Dq using the slope obtained by performing a linear regression of $log10(Cq(r))\;Vs.\;(q-1)log10(r)$. The case q=1 leads to the information dimension, that is treated separately in this package. The considerations discussed for the correlation dimension estimate are also valid for these generalized dimensions.

Value

The CalculateCorrDim returns the HRVData structure containing a corrDim object storing the results of the correlation sum (see corrDim) of the RR time series.

The EstimateCorrDim function estimates the correlation dimension of the RR time series by averaging the slopes of the embedding dimensions specified in the useEmbeddings parameter. The slopes are determined by performing a linear regression over the radius' range specified in regressionRange.If doPlot is TRUE, a graphic of the regression over the data is shown. The results are returned into the HRVData structure, under the NonLinearAnalysis list.

PlotCorrDim shows two graphics of the correlation integral: a log-log plot of the correlation sum Vs the radius and the local slopes of $log10(C(r))\;Vs\;log10(C(r)).$

Note

This function is based on the timeLag function from the nonlinearTseries package.

In order to run EstimateCorrDim, it is necessary to have performed the correlation sum before with ComputeCorrDim.

References

H. Kantz and T. Schreiber: Nonlinear Time series Analysis (Cambridge university press)

See Also

corrDim.

Examples

## Not run: 
 # ...
 hrv.data = CreateNonLinearAnalysis(hrv.data)
 hrv.data = CalculateCorrDim(hrv.data,indexNonLinearAnalysis=1, 
             minEmbeddingDim=2, maxEmbeddingDim=8,timeLag=1,minRadius=1,
             maxRadius=15, pointsRadius=20,theilerWindow=10,
             corrOrder=2,doPlot=FALSE)
 PlotCorrDim(hrv.data,indexNonLinearAnalysis=1)
 hrv.data = EstimateCorrDim(hrv.data,indexNonLinearAnalysis=1,
             useEmbeddings=6:8,regressionRange=c(1,10))

## End(Not run)

Description

Performs Detrended Fluctuation Analysis (DFA) on the RR time series, a widely used technique for detecting long range correlations in time series. These functions are able to estimate several scaling exponents from the time series being analyzed. These scaling exponents characterize short or long-term fluctuations, depending of the range used for regression (see details).

Usage

CalculateDFA(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  windowSizeRange = c(10, 300),
  npoints = 25,
  doPlot = TRUE
)

EstimateDFA(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  regressionRange = NULL,
  doPlot = TRUE
)

PlotDFA(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  ...
)

Arguments

Details

The Detrended Fluctuation Analysis (DFA) has become a widely used technique for detecting long range correlations in time series. The DFA procedure may be summarized as follows:

1. Integrate the time series to be analyzed. The time series resulting from the integration will be referred to as the profile.

2. Divide the profile into N non-overlapping segments.

3. Calculate the local trend for each of the segments using least-square regression. Compute the total error for each of the segments.

4. Compute the average of the total error over all segments and take its root square. By repeating the previous steps for several segment sizes (let's denote it by t), we obtain the so-called Fluctuation function $F(t)$.

5. If the data presents long-range power law correlations: $F(t) \sim t^\alpha$ and we may estimate using regression.

6. Usually, when plotting $\log(F(t))\;Vs\;log(t)$ we may distinguish two linear regions. By regression them separately, we obtain two scaling exponents, $\alpha_1$ (characterizing short-term fluctuations) and $\alpha_2$ (characterizing long-term fluctuations).

Steps 1-4 are performed using the CalculateDFA function. In order to obtain a estimate of some scaling exponent, the user must use the EstimateDFA function specifying the regression range (window sizes used to detrend the series). $\alpha_1$ is usually obtained by performing the regression in the $3<t<17$ range wheras that $\alpha_2$ is obtained in the $15<t<65$ range (However the F(t) function must be linear in these ranges for obtaining reliable results).

Value

The CalculateDFA returns a HRVData structure containing the computations of the Fluctuation function of the RR time series under the NonLinearAnalysis list.

The EstimateDFA function estimates an scaling exponent of the RR time series by performing a linear regression over the time steps' range specified in regressionRange. If doPlot is TRUE, a graphic of the regression over the data is shown. In order to run EstimateDFA, it is necessary to have performed the Fluctuation function computations before with ComputeDFA. The results are returned into the HRVData structure, under the NonLinearAnalysis list. Since it is possible to estimate several scaling exponents, depending on the regression range used, the scaling exponents are also stored into a list.

PlotDFA shows a graphic of the Fluctuation functions vs window's sizes.

Note

This function is based on the dfa function from the nonlinearTseries package.

See Also

dfa

Description

This function determines the minimum embedding dimension from a scalar time series using the algorithm proposed by L. Cao (see references).

Usage

CalculateEmbeddingDim(
  HRVData,
  numberPoints = 5000,
  timeLag = 1,
  maxEmbeddingDim = 15,
  threshold = 0.95,
  maxRelativeChange = 0.05,
  doPlot = TRUE
)

Arguments

Details

The Cao's algorithm uses 2 functions in order to estimate the embedding dimension from a time series: the E1(d) and the E2(d) functions, where d denotes the dimension.

E1(d) stops changing when d is greater than or equal to the embedding dimension, staying close to 1. On the other hand, E2(d) is used to distinguish deterministic signals from stochastic signals. For deterministic signals, there exists some d such that E2(d)!=1. For stochastic signals, E2(d) is approximately 1 for all the values.

Note

The current implementation of this function is fully written in R, based on the estimateEmbeddingDim function from the nonlinearTseries package. Thus it requires heavy computations and may be quite slow. The numberPoints parameter can be used for controlling the computational burden.

Future versions of the package will solve this issue.

References

Cao, L. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena, 110,1, pp. 43-50 (1997).

See Also

estimateEmbeddingDim.

Examples

## Not run: 
data(HRVProcessedData)
HRVData = HRVProcessedData
HRVData = SetVerbose(HRVData,T)
timeLag = CalculateTimeLag(HRVData,technique = "ami")
embeddingDim = CalculateEmbeddingDim(HRVData,
                                     timeLag = timeLag,
                                     maxEmbeddingDim = 15)

## End(Not run)

Description

Calculates the Energy in the bands of the Power Spectral Density (PSD).

Usage

CalculateEnergyInPSDBands(
  HRVData,
  indexFreqAnalysis = length(HRVData$FreqAnalysis),
  ULFmin = 0,
  ULFmax = 0.03,
  VLFmin = 0.03,
  VLFmax = 0.05,
  LFmin = 0.05,
  LFmax = 0.15,
  HFmin = 0.15,
  HFmax = 0.4
)

Arguments

Value

A vector containing the energy of the ULF, VLF, LF and HF bands in the PSD.

See Also

PlotPSD, CalculatePSD.

Examples

## Not run: 
data(HRVData)
HRVData=BuildNIHR(HRVData)
HRVData=FilterNIHR(HRVData)
# Frequency analysis requires interpolated data (except Lomb)
HRVData=InterpolateNIHR(HRVData)
HRVData=CreateFreqAnalysis(HRVData)
HRVData=CalculatePSD(HRVData,1,"pgram",doPlot = F)
# get Energy in the default ULF, VLF and LF frequency bands.
# We modify the limits for the HF band
CalculateEnergyInPSDBands(HRVData, 1, HFmin = 0.15, HFmax = 0.3) 

## End(Not run)

Description

WARNING: deprecated function. Calculates Fractal Dimension as indicated by Pincus

Usage

CalculateFracDim(HRVData, indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
    m = 10, tau = 3, Cra = 0.005, Crb = 0.75, N = 1000, verbose=NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register and now associated heart rate instantaneous values also, including the value of the Fractal Dimension

Note

This function is deprecated. We suggest the use of the CalculateCorrDim function instead, which is faster.

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011) S. M. Pincus, "Approximate entropy as a measure of system complexity," Mathematics 88, 2297-2301 (1991)

See Also

CalculateRfromCorrelation for finding r distance at which the correlation has a certain value

Description

Information dimension of the RR time series

Usage

CalculateInfDim(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  minEmbeddingDim = NULL,
  maxEmbeddingDim = NULL,
  timeLag = NULL,
  minFixedMass = 1e-04,
  maxFixedMass = 0.005,
  numberFixedMassPoints = 50,
  radius = 1,
  increasingRadiusFactor = 1.05,
  numberPoints = 500,
  theilerWindow = 100,
  doPlot = TRUE
)

EstimateInfDim(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  regressionRange = NULL,
  useEmbeddings = NULL,
  doPlot = TRUE
)

PlotInfDim(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  ...
)

Arguments

Details

The information dimension is a particular case of the generalized correlation dimension when setting the order q = 1. It is possible to demonstrate that the information dimension $D_1$ may be defined as: $D_1=lim_{r \rightarrow 0} <\log p(r)>/\log(r)$. Here, $p(r)$ is the probability of finding a neighbour in a neighbourhood of size $r$ and <> is the mean value. Thus, the information dimension specifies how the average Shannon information scales with the radius $r$.

In order to estimate $D_1$, the algorithm looks for the scaling behaviour of the average radius that contains a given portion (a "fixed-mass") of the total points in the phase space. By performing a linear regression of $\log(p)\;Vs.\;\log(<r>)$ (being $p$ the fixed-mass of the total points), an estimate of $D_1$ is obtained. The user should run the method for different embedding dimensions for checking if $D_1$ saturates.

The calculations for the information dimension are heavier than those needed for the correlation dimension.

Value

The CalculateCorrDim returns the HRVData structure containing a infDim object storing the results of the correlation sum (see infDim) of the RR time series.

The EstimateInfDim function estimates the information dimension of the RR time series by averaging the slopes of the correlation sums with q=1. The slopes are determined by performing a linear regression over the radius' range specified in regressionRange.If doPlot is TRUE, a graphic of the regression over the data is shown. The results are returned into the HRVData structure, under the NonLinearAnalysis list.

PlotInfDim shows a graphics of the correlation sum with q=1.

Note

In order to run EstimateInfDim, it is necessary to have performed the correlation sum before with ComputeInfDim.

References

H. Kantz and T. Schreiber: Nonlinear Time series Analysis (Cambridge university press)

See Also

CalculateCorrDim.

Description

Functions for estimating the maximal Lyapunov exponent of the RR time series.

Usage

CalculateMaxLyapunov(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  minEmbeddingDim = NULL,
  maxEmbeddingDim = NULL,
  timeLag = NULL,
  radius = 2,
  theilerWindow = 100,
  minNeighs = 5,
  minRefPoints = 500,
  numberTimeSteps = 20,
  doPlot = TRUE
)

EstimateMaxLyapunov(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  regressionRange = NULL,
  useEmbeddings = NULL,
  doPlot = TRUE
)

PlotMaxLyapunov(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  ...
)

Arguments

Details

It is a well-known fact that close trajectories diverge exponentially fast in a chaotic system. The averaged exponent that determines the divergence rate is called the Lyapunov exponent (usually denoted with $\lambda$). If $\delta(0)$ is the distance between two Takens' vectors in the embedding.dim-dimensional space, we expect that the distance after a time $t$ between the two trajectories arising from this two vectors fulfills:

$\delta (n) \sim \delta (0)\cdot exp(\lambda \cdot t)$

The lyapunov exponent is estimated using the slope obtained by performing a linear regression of $S(t)=\lambda \cdot t \sim log(\delta (t)/\delta (0))$ on $t$. $S(t)$ will be estimated by averaging the divergence of several reference points.

The user should plot $S(t) Vs t$ when looking for the maximal lyapunov exponent and, if for some temporal range $S(t)$ shows a linear behaviour, its slope is an estimate of the maximal Lyapunov exponent per unit of time. The estimate routine allows the user to get always an estimate of the maximal Lyapunov exponent, but the user must check that there is a linear region in the $S(t) Vs t$. If such a region does not exist, the estimation should be discarded. The user should also run the method for different embedding dimensions for checking if $D_1$ saturates.

Value

The CalculateMaxLyapunov returns a HRVData structure containing the divergence computations of the RR time series under the NonLinearAnalysis list.

The EstimateMaxLyapunov function estimates the maximum Lyapunov exponent of the RR time series by performing a linear regression over the time steps' range specified in regressionRange.If doPlot is TRUE, a graphic of the regression over the data is shown. The results are returned into the HRVData structure, under the NonLinearAnalysis list.

PlotMaxLyapunov shows a graphic of the divergence Vs time

Note

This function is based on the maxLyapunov function from the nonlinearTseries package.

In order to run EstimateMaxLyapunov, it is necessary to have performed the divergence computations before with ComputeMaxLyapunov.

References

Eckmann, Jean-Pierre and Kamphorst, S Oliffson and Ruelle, David and Ciliberto, S and others. Liapunov exponents from time series. Physical Review A, 34-6, 4971–4979, (1986).

Rosenstein, Michael T and Collins, James J and De Luca, Carlo J.A practical method for calculating largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 65-1, 117–134, (1993).

See Also

maxLyapunov

Examples

## Not run: 
# ...
hrv.data = CreateNonLinearAnalysis(hrv.data)
hrv.data = CalculateMaxLyapunov(hrv.data,indexNonLinearAnalysis=1,
                                 minEmbeddingDim=5,
                                 maxEmbeddingDim = 5,
                                 timeLag=1,radius=10,
                                 theilerWindow=100, doPlot=FALSE)
PlotMaxLyapunov(hrv.data,indexNonLinearAnalysis=1)
hrv.data = EstimateMaxLyapunov(hrv.data,indexNonLinearAnalysis=1, 
                               regressionRange=c(1,10))

## End(Not run)

Description

Calculates power of the heart rate signal at ULF, VLF, LF and HF bands

Usage

CalculatePowerBand(HRVData,
                   indexFreqAnalysis = length(HRVData$FreqAnalysis),
                   size, shift, sizesp = NULL, scale = "linear",
                   ULFmin = 0, ULFmax = 0.03,
                   VLFmin = 0.03, VLFmax = 0.05,
                   LFmin = 0.05, LFmax = 0.15,
                   HFmin = 0.15, HFmax = 0.4,
                   type = c("fourier", "wavelet"), wavelet = "d4",
                   bandtolerance = 0.01, relative = FALSE,
                   verbose = NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register, associated heart rate instantaneous values, filtered heart rate signal equally spaced, and the analysis structure including spectral power at different bands of the heart rate signal

Note

An example including all the necessary steps to obtain the power
bands of a wfdb register is giving below:

##Reading a wfdb register and storing into a data structure:
md = CreateHRVData(Verbose = TRUE)
md = LoadBeatWFDB(md, RecordName = "register_name",
RecordPath = "register_path")

##Calculating heart rate signal:
md = BuildNIHR(md)

##Filtering heart rate signal:
md = FilterNIHR(md)

##Interpolating heart rate signal:
md = InterpolateNIHR(md)

##Calculating spectrogram and power per band using fourier
analysis:
md = CreateFreqAnalysis(md)
md = CalculatePowerBand(md, indexFreqAnalysis = 1, size = 120,
shift = 10, sizesp = 1024)

##Calculating spectrogram and power per band using wavelet analysis:
md = CreateFreqAnalysis(md)
md = CalculatePowerBand(md, indexFreqAnalysis = 2, type="wavelet",
wavelet="la8",bandtolerance=0.0025)

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, L., A.J. Mendez, M.J. Lado, D.N. Olivieri, X.A. Vila, and I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103(1):39-50, july 2011.

Description

Estimate the Power Spectral Density (PSD) of the RR time series.

Usage

CalculatePSD(
  HRVData,
  indexFreqAnalysis = length(HRVData$FreqAnalysis),
  method = c("pgram", "ar", "lomb"),
  doPlot = T,
  ...
)

Arguments

Details

The "pgram" and "ar" methods use the spec.pgram and spec.ar functions. Thus, the same arguments used in spec.pgram or spec.ar can be used when method is "pgram" or "ar", respectively. The "lomb" is based in the lsp and thus it accepts the same parameters as this function.

Value

The CalculatePSD returns the HRVData structure containing a periodogram field storing and PSD estimation of the RR time series. When the "pgram" and "ar" methods are used the periodogram field is an object of class "spec". If "lomb" is used, the periodogram field is just a list. In any case the periodogram field will contain:

• freq: vector of frequencies at which the spectral density is estimated.

• spec: spectral density estimation

• series: name of the series

• method: method used to calculate the spectrum

See Also

spectrum, PlotPSD.

Examples

## Not run: 
data(HRVData)
HRVData=BuildNIHR(HRVData)
HRVData=FilterNIHR(HRVData)
# Frequency analysis requires interpolated data (except Lomb)
HRVData=InterpolateNIHR(HRVData)
# Create a different freqAnalysis for each method
HRVData=CreateFreqAnalysis(HRVData)
HRVData=CalculatePSD(HRVData,1,"pgram",doPlot = F)

HRVData=CreateFreqAnalysis(HRVData)
HRVData=CalculatePSD(HRVData,2,"pgram",spans=9, doPlot = F)

HRVData=CreateFreqAnalysis(HRVData)
HRVData=CalculatePSD(HRVData,3,"ar",doPlot = F)

HRVData=CreateFreqAnalysis(HRVData)
HRVData=CalculatePSD(HRVData,4,"lomb",doPlot = F)
# Plot the results
layout(matrix(c(1,2,3,4), 2, 2, byrow = TRUE))
PlotPSD(HRVData,1)
PlotPSD(HRVData,2)
PlotPSD(HRVData,3)
PlotPSD(HRVData,4)

## End(Not run)

Description

WARNING: deprecated function. Calculates ra and rb distances that verify that their correlation values are Cra and Crb

Usage

CalculateRfromCorrelation(HRVData, Data, m, tau, Cra, Crb)

Arguments

Value

Returns a 2 by 2 matrix containing ra and rb distance in the first row and their exact correlation values in the second row

Note

This function is used in the CalculateFracDim function, which is deprecated. We suggest the use of the CalculateCorrDim function instead of CalculateFracDim.

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, L., A.J. Mendez, M.J. Lado, D.N. Olivieri, X.A. Vila, and I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103(1):39-50, july 2011. S. M. Pincus, "Approximate entropy as a measure of system complexity," Mathematics 88, 2297-2301 (1991)

See Also

CalculateFracDim

Description

These functions measure the complexity of the RR time series. Large values of the Sample Entropy indicate high complexity whereas that smaller values characterize more regular signals.

Usage

CalculateSampleEntropy(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  doPlot = TRUE
)

EstimateSampleEntropy(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  regressionRange = NULL,
  useEmbeddings = NULL,
  doPlot = TRUE
)

PlotSampleEntropy(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
  ...
)

Arguments

Details

The sample entropy is computed using:

$h_q(m,r) = log(C_q(m,r)/C_{q}(m+1,r))$

where m is the embedding dimension and r is the radius of the neighbourhood. When computing the correlation dimensions we use the linear regions from the correlation sums in order to do the estimates. Similarly, the sample entropy $h_q(m,r)$ should not change for both various m and r.

Value

The CalculateSampleEntropy returns a HRVData structure containing the sample entropy computations of the RR time series under the NonLinearAnalysis list.

The EstimateSampleEntropy function estimates the sample entropy of the RR time series by performing a linear regression over the radius' range specified in regressionRange. If doPlot is TRUE, a graphic of the regression over the data is shown. In order to run EstimateSampleEntropy, it is necessary to have performed the sample entropy computations before with ComputeSampleEntropy. The results are returned into the HRVData structure, under the NonLinearAnalysis list.

PlotSampleEntropy shows a graphic of the sample entropy computations.

Note

In order to run this functions, it is necessary to have used the CalculateCorrDim function.

This function is based on the sampleEntropy function from the nonlinearTseries package.

References

H. Kantz and T. Schreiber: Nonlinear Time series Analysis (Cambridge university press)

See Also

sampleEntropy

Examples

## Not run: 
# ...
hrv.data = CreateNonLinearAnalysis(hrv.data)
hrv.data = CalculateCorrDim(hrv.data,indexNonLinearAnalysis=1,minEmbeddingDim=2,
                            maxEmbeddingDim=8,timeLag=1,minRadius=1,maxRadius=15,
                            pointsRadius=20,theilerWindow=10,corrOrder=2,doPlot=FALSE)
hrv.data = CalculateSampleEntropy(hrv.data,indexNonLinearAnalysis=1,doPlot=FALSE)
PlotSampleEntropy(hrv.data,indexNonLinearAnalysis=1)
hrv.data = EstimateSampleEntropy(hrv.data,indexNonLinearAnalysis=1,regressionRange=c(6,10))

## End(Not run)

Description

Calculates the spectrogram of the heart rate signal after filtering and interpolation in a window of a certain size

Usage

CalculateSpectrogram(HRVData, size, shift, sizesp = 1024, verbose=NULL)

Arguments

Value

Returns the spectrogram of the heart rate signal

Note

An example including all the necessary steps to obtain the spectrogram
of a wfdb register is giving below:

##Reading a wfdb register and storing into a data structure:
md = CreateHRVData(Verbose = TRUE)
md = LoadBeatWFDB(md, RecordName = "register_name",
RecordPath = "register_path", verbose = TRUE)

##Calculating heart rate signal:
md = BuildNIHR(md)

##Filtering heart rate signal:
md = FilterNIHR(md)

##Interpolating heart rate signal:
md = InterpolateNIHR(md)

##Calculating spectrogram:
CalculateSpectrogram(md, size = 120, shift = 10, sizesp = 1024)

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

Given a time series (timeSeries), an embedding dimension (m) and a time lag (timeLag), the $n^{th}$ Takens' vector is defined as

$T[n]={timeSeries[n], timeSeries[n+ timeLag],...timeSeries[n+m*timeLag]}.$

This function estimates an appropiate time lag by using the autocorrelation or the average mutual information (AMI) function.

Usage

CalculateTimeLag(
  HRVData,
  technique = c("acf", "ami"),
  method = c("first.e.decay", "first.zero", "first.minimum", "first.value"),
  value = 1/exp(1),
  lagMax = NULL,
  doPlot = TRUE,
  ...
)

Arguments

Details

A basic criteria for estimating a proper time lag is based on the following reasoning: if the time lag used to build the Takens' vectors is too small, the coordinates will be too highly temporally correlated and the embedding will tend to cluster around the diagonal in the phase space. If the time lag is chosen too large, the resulting coordinates may be almost uncorrelated and the resulting embedding will be very complicated. Thus, the autocorrelation function can be used for estimating an appropiate time lag of a time series. However, it must be noted that the autocorrelation is a linear statistic, and thus it does not take into account nonlinear dynamical correlations. To take into account nonlinear correlations the average mutual information (AMI) can be used. Independently of the technique used to compute the correlation, the time lag can be selected in a variety of ways:

• Select the time lag where the autocorrelation/AMI function decays to 0 (first.zero method). This method is not appropriate for the AMI function, since it only takes positive values.

• Select the time lag where the autocorrelation/AMI function decays to 1/e of its value at zero (first.e.decay method).

• Select the time lag where the autocorrelation/AMI function reaches its first minimum (first.minimum method).

• Select the time lag where the autocorrelation/AMI function decays to the value specified by the user (first.value method and value parameter).

Value

The estimated time lag.

Note

If the autocorrelation/AMI function does not cross the specifiged value, an error is thrown. This may be solved by increasing the lag.max or selecting a higher value to which the autocorrelation/AMI function may decay.

This function is based on the timeLag function from the nonlinearTseries package.

References

H. Kantz and T. Schreiber: Nonlinear Time series Analysis (Cambridge university press)

See Also

timeLag,mutualInformation .

Examples

## Not run: 
data(HRVProcessedData)
HRVData = HRVProcessedData
HRVData = SetVerbose(HRVData,T)
timeLag = CalculateTimeLag(HRVData,technique = "ami")
embeddingDim = CalculateEmbeddingDim(HRVData,
                                     timeLag = timeLag,
                                     maxEmbeddingDim = 15)

## End(Not run)

Description

Creates data analysis structure that stores the information extracted from a variability analysis of heart rate signal and joins it to HRVData as a member of a list

Usage

CreateFreqAnalysis(HRVData, verbose=NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register, associated heart rate instantaneous values, filtered heart rate signal equally spaced, and a new analysis structure as a member of a list

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

See Also

CreateHRVData

Description

Creates data structure that stores the beats register and all the information obtained from it

Usage

CreateHRVData(Verbose = FALSE)

Arguments

Value

Returns HRVData, the structure that will contain beat positions register, associated heart rate instantaneous values, filtered heart rate signal equally spaced, and one or more analysis structures

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

See Also

CreateFreqAnalysis, CreateTimeAnalysis, CreateNonLinearAnalysis

Description

Creates data analysis structure that stores the information extracted from a non linear analysis of ECG signal and joins it to HRVData as a member of a list

Usage

CreateNonLinearAnalysis(HRVData, verbose=NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register, associated heart rate instantaneous values, filtered heart rate signal equally spaced, and a new analysis structure as a member of a list

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

See Also

CreateHRVData

Description

Creates data analysis structure that stores the information extracted from a time analysis of ECG signal and joins it to HRVData as a member of a list

Usage

CreateTimeAnalysis(HRVData, size=300, numofbins=NULL, interval=7.8125, verbose=NULL )

Arguments

Value

Returns HRVData, the structure that contains beat positions register, associated heart rate instantaneous values, filtered heart rate signal equally spaced, and a new analysis structure as a member of a list

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

See Also

CreateHRVData

Description

Plots non-interpolated instantaneous heart rate for manual removing of outliers

Usage

EditNIHR(HRVData, scale = 1, verbose=NULL)

Arguments

Value

Returns Data, the structure that contains beat positions register, and manually edited associated heart rate instantaneous values

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

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

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

spectrum,lsp, CalculatePSD.

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)

Description

Extracts a temporal subset between the times starttime and endtime.

Usage

ExtractTimeSegment(HRVData, starttime, endtime)

Arguments

Details

If the HRVData contains episodes, beats or RR time series, these will be also extracted into the new HRV structure. On the other hand, all the analysis stored in the original structure will be lost.

Value

A new HRVData structure containing the temporal data within the specified range.

Author(s)

Leandro Rodriguez-Linares

Examples

## Not run: 
data(HRVProcessedData)
# Rename for convenience
HRVData <- HRVProcessedData
PlotNIHR(HRVData)
newHRVData <- ExtractTimeSegment(HRVData,2000,4000)
PlotNIHR(newHRVData)

## End(Not run)

Description

An algorithm that uses adaptive thresholds for rejecting those beats different from the given threshold more than a certain value. The rule for beat acceptation or rejection is to compare with previous, following and with the updated mean. We apply also a comparison with acceptable physiological values (default values 25 and 200 bpm).

Usage

FilterNIHR(HRVData, long=50, last=13, minbpm=25, maxbpm=200, mini=NULL, 
		maxi=NULL, fixed=NULL, verbose=NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register, associated heart rate instantaneous values also, and now filtered heart rate signal

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

X. Vila, F. Palacios, J. Presedo, M. Fernandez-Delgado, P. Felix, S. Barro, "Time-Frequency analysis of heart-rate variability," IEEE Eng. Med. Biol. Magazine 16, 119-125 (1997) L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

Creates new episodes, or annotated physiological events, from existing ones and stores them into the data structure containing the beat positions

Usage

GenerateEpisodes(HRVData, NewBegFrom, NewEndFrom, DispBeg, DispEnd, 
		OldTag = "", NewTag = "", verbose=NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register and new episodes information

Note

##Example of arguments for creating episodes displaced one
minute before old ones:
##NewBegFrom = "Beg", NewEndFrom = "End", DispBeg = -60,
DispEnd = -60
##Example of arguments for creating episodes just after previous
ones of 1 minute length:
##NewBegFrom = "End", NewEndFrom = "End", DispBeg = 0,
DispEnd = 60

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

Calculates the spectrogram bands in normalized units

Usage

getNormSpectralUnits(
  HRVData,
  indexFreqAnalysis = length(HRVData$FreqAnalysis),
  VLFnormalization = T
)

Arguments

Details

The default behaviour of this function computes the normalized power time series in the LF and HF bands following the Task Force recommendations:

$normalized\_LF = LF\_power / (total\_power - VLF\_power - ULF\_power)$

$normalized\_HF = HF\_power / (total\_power - VLF\_power -ULF\_power)$

If VLFnormalization is set to FALSE, the functions computes:

$normalized\_VLF = VLF\_power / (total\_power - ULF\_power)$

$normalized\_LF = LF\_power / (total\_power - ULF\_power)$

$normalized\_HF = HF\_power / (total\_power - ULF\_power)$

The resulting time series are returned in a list. Note that before using this function, the spectrogram should be computed with the CalculatePowerBand function.

Value

The getNormSpectralUnits returns a list storing the resulting normalized power-band series. Note that this list is not stored in the HRVData structure.

References

Camm, A. J., et al. "Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology." Circulation 93.5 (1996): 1043-1065.

Examples

## Not run: 
# load some data...
data(HRVProcessedData)
hd = HRVProcessedData
# Perform some spectral analysis and normalize the results
hd = CreateFreqAnalysis(hd)
hd = CalculatePowerBand(hd,indexFreqAnalysis = 1,shift=30,size=60)
normUnits = getNormSpectralUnits(hd)
# plot the normalized time series
par(mfrow=c(2,1))
plot(normUnits$Time, normUnits$LF, xlab="Time", ylab="normalized LF",
     main="normalized LF",type="l")
plot(normUnits$Time, normUnits$HF, xlab="Time", ylab="normalized HF",
     main="normalized HF",type="l")
par(mfrow=c(1,1))


## End(Not run)

Description

HRVData structure containing the occurrence times of the hearbeats of patient suffering from paraplegia and hypertension. The subject from whom the HR was obtained is a patient suffering from paraplegia and hypertension (systolic blood pressure above 200 mmHg). During the recording, he is supplied with prostaglandin E1 (a vasodilator that is rarely employed) and systolic blood pressure fell to 100 mmHg for over an hour. Then, the blood pressure was slowly recovering until 150 mmHg, more or less

Usage

data(HRVData)

Format

A HRVData structure containing the occurrence times of the heartbeats

See Also

HRVProcessedData

Description

HRV data containing the heart rhythm of patient suffering from paraplegia and hypertension. The subject from whom the HR was obtained is a patient suffering from paraplegia and hypertension (systolic blood pressure above 200 mmHg). During the recording, he is supplied with prostaglandin E1 (a vasodilator that is rarely employed) and systolic blood pressure fell to 100 mmHg for over an hour. Then, the blood pressure was slowly recovering until 150 mmHg, more or less

Usage

data(HRVProcessedData)

Format

A HRVData structure containing the interpolated and filtered HR series

See Also

HRVData

Description

WARNING: deprecated function. The Integral correlation is calculated for every vector of the m-dimensional space

Usage

IntegralCorrelation(HRVData, Data, m, tau, r)

Arguments

Value

Returns the value of the average of IntegralCorrelations

Note

This function is used in the CalculateApEn function, which is deprecated. We suggest the use of the CalculateSampleEntropy function instead of CalculateApEn.

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

See Also

BuildTakensVector

Description

An algorithm to obtain a heart rate signal with equally spaced values at a certain sampling frequency

Usage

InterpolateNIHR(HRVData, freqhr = 4, method = c("linear", "spline"), verbose=NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register, associated heart rate instantaneous values also, and filtered heart rate signal equally spaced

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

Lists episodes included in a RHRV record

Usage

ListEpisodes(HRVData, TimeHMS = FALSE)

Arguments

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

Loads the information of apnea episodes and stores it into the data structure containing the beat positions and other related information

Usage

LoadApneaWFDB(HRVData, RecordName, RecordPath = ".", Tag = "APNEA", 
		verbose=NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register and other related information and apnea episodes information

Note

An example including all the steps to download a record from Physionet and load its content and the Apnea annotations is included below:

dirorig <- "http://www.physionet.org/physiobank/database/apnea-ecg/"
files <- c("a01.hea", "a01.apn", "a01.qrs")
filesorig <- paste(dirorig, files, sep = "")
for (i in 1:length(files))
download.file(filesorig[i], files[i])
hrv.data <- CreateHRVData()
hrv.data <- LoadBeatWFDB(hrv.data, "a01")
hrv.data <- LoadApneaWFDB(hrv.data, "a01")

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

Reads the specific file with data of beat positions and stores the values in a data structure

Usage

LoadBeat(fileType, HRVData, Recordname, RecordPath = ".", 
		annotator = "qrs", scale = 1, datetime = "1/1/1900 0:0:0", 
		annotationType = "QRS", verbose = NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register

Author(s)

I. Garcia

References

L. Rodriguez-Linares, L., A.J. Mendez, M.J. Lado, D.N. Olivieri, X.A. Vila, and I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103(1):39-50, july 2011.

Description

Reads a Suunto Ambit XML file with data of beat positions and stores the values in a data structure

Usage

LoadBeatAmbit(HRVData, RecordName, RecordPath = ".", verbose = NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register

Author(s)

Matti Lassila

References

L. Rodriguez-Linares, X. Vila, A. Mendez, M. Lado, D. Olivieri, "RHRV: An R-based software package for heart rate variability analysis of ECG recordings," 3rd Iberian Conference in Systems and Information Technologies (CISTI 2008), Proceedings I, 565-573, ISBN: 978-84-612-4476-8 (2008)

Description

Reads an ascii file with data of beat positions and stores the values in a data structure. A segment of a file can be loaded making use of the "starttime" and "endtime" arguments.

Usage

LoadBeatAscii(HRVData, RecordName, RecordPath=".", scale = 1, starttime=NULL,
      endtime=NULL, datetime = "1/1/1900 0:0:0", verbose = NULL)

Arguments

Value

Loads beats positions into the structure that contains RHRV information. The file containing the heartbeats positions must be a single column file with no headers. Each line should denote the occurrence time of each heartbeat. An example of a valid file could be the following:
0
0.3280001
0.7159996
1.124
1.5
1.88
(...)

Author(s)

A. Mendez, L. Rodriguez, A. Otero, C.A. Garcia, X. Vila, M. Lado

References

L. Rodriguez-Linares, L., A.J. Mendez, M.J. Lado, D.N. Olivieri, X.A. Vila, and I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103(1):39-50, july 2011.

Description

Basically, this algorithm reads the annotation file for the ECG register, and stores the information obtained in a data structure.

Usage

LoadBeatEDFPlus(HRVData, RecordName, RecordPath = ".", 
		annotationType ="QRS", verbose = NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register

Author(s)

I. Garcia

References

L. Rodriguez-Linares, L., A.J. Mendez, M.J. Lado, D.N. Olivieri, X.A. Vila, and I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103(1):39-50, july 2011.

Description

Reads a Polar file with data of beat positions and stores the values in a data structure

Usage

LoadBeatPolar(HRVData, RecordName, RecordPath=".", verbose = NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register

Author(s)

I. Garcia

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

Reads an ascii file containing RR values, i.e. distances between two successive beats.

Usage

LoadBeatRR(HRVData, RecordName, RecordPath=".", scale = 1, 
		  datetime = "1/1/1900 0:0:0", verbose = NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register

Author(s)

I. Garcia

References

L. Rodriguez-Linares, L., A.J. Mendez, M.J. Lado, D.N. Olivieri, X.A. Vila, and I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103(1):39-50, july 2011.

Description

Reads a Suunto file with data of beat positions and stores the values in a data structure

Usage

LoadBeatSuunto(HRVData, RecordName, RecordPath = ".", verbose = NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register

Author(s)

I. Garcia

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

Stores the beat positions from an R vector under the HRVData data structure.

Usage

LoadBeatVector(HRVData, beatPositions, scale = 1, datetime = "1/1/1900 0:0:0")

Arguments

Value

A HRVData structure containing the heartbeat positions from the beatPositions vector.

Examples

## Not run: 
hd = CreateHRVData()
hd = LoadBeatVector(hd, 
     c(0.000, 0.328, 0.715, 0.124, 1.50,1.880, 2.268, 2.656))
hd = BuildNIHR(hd)
# ... continue analyzing the recording

## End(Not run)

Description

Basically, this algorithm reads the annotation file for the ECG register, and stores the information obtained in a data structure.

Usage

LoadBeatWFDB(HRVData, RecordName, RecordPath = ".", annotator = "qrs", 
		verbose=NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register

Author(s)

I. Garcia

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

Loads the information of episodes, or annotated physiological events, and stores it into the data structure containing the beat positions

Usage

LoadEpisodesAscii(HRVData, FileName, RecordPath=".", Tag="", InitTime="0:0:0",
verbose=NULL,header = TRUE)

Arguments

Value

Returns HRVData, the structure that contains beat positions register and episodes information

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

Reads the header file for the ECG register, and stores the information obtained in a data structure

Usage

LoadHeaderWFDB(HRVData, RecordName, RecordPath = ".", verbose=NULL)

Arguments

Value

Returns Data, the structure that contains beat positions register and data extracted from header file

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

This function allow users to modify the parameters that define episodes: Tags, InitTimes, Durations and Values.

Episodes can be selected by Tags or Indexes (or both) and more than one episodes' characteristics can be modified within the same call.

When modifying more than one episode, vectors of new parameters are recycled.

After the modification has been made, duplicate episodes are removed and they are reordered by increasing InitTimes.

Usage

ModifyEpisodes(HRVData, Tags=NULL, Indexes=NULL, NewInitTimes=NULL,
NewTags=NULL, NewDurations=NULL ,NewValues=NULL)

Arguments

Value

Returns HRVData, the structure that contains beat positions register and new episodes information

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

Description

Nonlinearity tests

Usage

NonlinearityTests(
  HRVData,
  indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis)
)

Arguments

Details

This function runs a set of nonlinearity tests on the RR time series implemented in other R packages including:

• Teraesvirta's neural metwork test for nonlinearity (terasvirta.test).

• White neural metwork test for nonlinearity (white.test).

• Keenan's one-degree test for nonlinearity (Keenan.test).

• Perform the McLeod-Li test for conditional heteroscedascity (ARCH). (McLeod.Li.test).

• Perform the Tsay's test for quadratic nonlinearity in a time series. (Tsay.test).

• Perform the Likelihood ratio test for threshold nonlinearity. (tlrt).

Value

A HRVData structure containing a NonlinearityTests field storing the results of each of the tests. The NonlinearityTests list is stored under the NonLinearAnalysis structure.

Description

Function for denoising the RR time series using nonlinear analysis techniques.

Usage

NonLinearNoiseReduction(
  HRVData,
  embeddingDim = NULL,
  radius = NULL,
  ECGsamplingFreq = NULL
)

Arguments

Details

This function takes the RR time series and denoises it. The denoising is achieved by averaging each Takens' vector in an m-dimensional space with his neighbours (time lag=1). Each neighbourhood is specified with balls of a given radius (max norm is used).

Value

A HRVData structure containing the denoised RR time series.

Note

This function is based on the nonLinearNoiseReduction function from the nonlinearTseries package.

References

H. Kantz and T. Schreiber: Nonlinear Time series Analysis (Cambridge university press)

See Also

nonLinearNoiseReduction

Description

Add episodic information to the current plot

Usage

OverplotEpisodes(
  HRVData,
  Tags = NULL,
  Indexes = NULL,
  epColorPalette = NULL,
  eplim,
  lty = 2,
  markEpisodes = T,
  ymark,
  showEpLegend = T,
  epLegendCoords = NULL,
  Tag = NULL,
  ...
)

Arguments

Examples

## Not run: 
# Read file "a03" from the physionet apnea-ecg database
library(RHRV)
HRVData <- CreateHRVData()
HRVData <- LoadBeatWFDB(HRVData,RecordName="test_files/WFDB/a03")
HRVData <- LoadApneaWFDB(HRVData,RecordName="test_files/WFDB/a03")
# Add other type of episode for a more complete example (this episode does
# not have any physiological meaning)
HRVData <- AddEpisodes(HRVData,InitTimes=c(4500),Durations=c(1000), 
                       Tags="Other", Values = 1)
HRVData <- BuildNIHR(HRVData)
HRVData <- FilterNIHR(HRVData)
HRVData <- InterpolateNIHR(HRVData)


PlotHR(HRVData)
OverplotEpisodes(HRVData,ymark=c(150,151),eplim=c(20,150))

# Change some default parameters
PlotHR(HRVData)
OverplotEpisodes(HRVData,ymark=c(150,151),eplim=c(20,150),
                 epLegendCoords=c(25000,150), lty=5, 
                 epColorPalette=c("blue","green"))
                 
# Use episodic information with the spectrogram... In order to obtain a proper
# representation of the episodes we need to avoid the use of the spectrogram
# legend
sp <- PlotSpectrogram(HRVData, size=600, shift=60, freqRange=c(0,0.05),
                      showLegend=F);
OverplotEpisodes(HRVData, markEpisodes=T, ymark=c(0.04,0.0401),
                 eplim=c(0,0.04), Tags="APNEA",
                 epColorPalette = c("white"), lwd=3)

## End(Not run)

Description

Plots in a simple way the interpolated instantaneous heart rate signal.

Usage

PlotHR(
  HRVData,
  Tags = NULL,
  Indexes = NULL,
  main = "Interpolated instantaneous heart rate",
  xlab = "time (sec.)",
  ylab = "HR (beats/min.)",
  type = "l",
  ylim = NULL,
  Tag = NULL,
  verbose = NULL,
  ...
)

Arguments

Details

PlotHR

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila, C.A. Garcia

References

L. Rodriguez-Linares, L., A.J. Mendez, M.J. Lado, D.N. Olivieri, X.A. Vila, and I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103(1):39-50, july 2011.

Description

Plots in a simple way the non-interpolated instantaneous heart rate signal

Usage

PlotNIHR(
  HRVData,
  Tags = NULL,
  Indexes = NULL,
  main = "Non-interpolated instantaneous heart rate",
  xlab = "time (sec.)",
  ylab = "HR (beats/min.)",
  type = "l",
  ylim = NULL,
  Tag = NULL,
  verbose = NULL,
  ...
)

Arguments

Details

PlotNIHR

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila, C.A. Garcia

References

L. Rodriguez-Linares, L., A.J. Mendez, M.J. Lado, D.N. Olivieri, X.A. Vila, and I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103(1):39-50, july 2011.

Description

Plots the power of the heart rate signal at different bands of physiological interest.

Usage

PlotPowerBand(
  HRVData,
  indexFreqAnalysis = length(HRVData$FreqAnalysis),
  normalized = FALSE,
  hr = FALSE,
  ymax = NULL,
  ymaxratio = NULL,
  ymaxnorm = 1,
  Tags = NULL,
  Indexes = NULL,
  Tag = NULL,
  verbose = NULL
)

Arguments

Details

PlotPowerBand

Note

See PlotSinglePowerBand for a more flexible function for plotting power bands.

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila

References

L. Rodriguez-Linares, L., A.J. Mendez, M.J. Lado, D.N. Olivieri, X.A. Vila, and I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103(1):39-50, july 2011.

See Also

CalculatePowerBand for power calculation and PlotSinglePowerBand

Examples

## Not run: 
# Reading a wfdb register and storing into a data structure:
md = CreateHRVData(Verbose = TRUE)
md = LoadBeatWFDB(md, RecordName = "register_name", 
                  RecordPath = "register_path")

# Calculating heart rate signal:md = BuildNIHR(md)

# Filtering heart rate signal:
md = FilterNIHR(md)
# Interpolating heart rate signal:
md = InterpolateNIHR(md)
# Calculating spectrogram and power per band:
md = CreateFreqAnalysis(md)
md = CalculatePowerBand(md, indexFreqAnalysis = 1, size = 120, 
                        shift = 10, sizesp = 1024)
# Plotting Power per Band
PlotPowerBand(md, hr = TRUE, ymax = 700000, ymaxratio = 4)

## End(Not run)

Description

Plot the PSD estimate of the RR time series distinguishing the different frequency bands with different colurs.

Usage

PlotPSD(
  HRVData,
  indexFreqAnalysis = length(HRVData$FreqAnalysis),
  ULFmin = 0,
  ULFmax = 0.03,
  VLFmin = 0.03,
  VLFmax = 0.05,
  LFmin = 0.05,
  LFmax = 0.15,
  HFmin = 0.15,
  HFmax = 0.4,
  log = "y",
  type = "l",
  xlab = "Frequency (Hz) ",
  ylab = "Spectrum",
  main = NULL,
  xlim = c(min(ULFmin, ULFmax, VLFmin, VLFmax, LFmin, LFmax, HFmin, HFmax), max(ULFmin,
    ULFmax, VLFmin, VLFmax, LFmin, LFmax, HFmin, HFmax)),
  ylim = NULL,
  addLegend = TRUE,
  addSigLevel = TRUE,
  usePalette = c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442"),
  ...
)

Arguments

See Also

spectrum, lsp, CalculatePSD.

Examples

## Not run: 
data(HRVData)
HRVData=BuildNIHR(HRVData)
HRVData=FilterNIHR(HRVData)
# Frequency analysis requires interpolated data (except Lomb)
HRVData=InterpolateNIHR(HRVData)
# Create a different freqAnalysis for each method
HRVData=CreateFreqAnalysis(HRVData)
HRVData=CalculatePSD(HRVData,1,"pgram",doPlot = F)

HRVData=CalculatePSD(HRVData,2,"pgram",spans=9,doPlot = F)

HRVData=CreateFreqAnalysis(HRVData)
HRVData=CalculatePSD(HRVData,3,"ar",doPlot = F)

HRVData=CreateFreqAnalysis(HRVData)
HRVData=CalculatePSD(HRVData,4,"lomb",doPlot = F)
# Plot the results
layout(matrix(c(1,2,3,4), 2, 2, byrow = TRUE))
PlotPSD(HRVData,1)
PlotPSD(HRVData,2)
PlotPSD(HRVData,3)
PlotPSD(HRVData,4)

## End(Not run)

Description

Plots a concrete power band computed by the CalculatePowerBand function

Usage

PlotSinglePowerBand(
  HRVData,
  indexFreqAnalysis = length(HRVData$FreqAnalysis),
  band = c("LF", "HF", "ULF", "VLF", "LF/HF"),
  normalized = FALSE,
  main = paste(band, "Power Band"),
  xlab = "Time",
  ylab = paste("Power in", band),
  type = "l",
  Tags = NULL,
  Indexes = NULL,
  eplim = NULL,
  epColorPalette = NULL,
  markEpisodes = TRUE,
  ymark = NULL,
  showEpLegend = TRUE,
  epLegendCoords = NULL,
  Tag = NULL,
  ...
)

Arguments

See Also

CalculatePowerBand for power calculation

Examples

## Not run: 

# Read file "a03" from the physionet apnea-ecg database
library(RHRV)
HRVData <- CreateHRVData()
HRVData <- LoadBeatWFDB(HRVData,RecordName="test_files/WFDB/a03")
HRVData <- LoadApneaWFDB(HRVData,RecordName="test_files/WFDB/a03")
# Calculating heart rate signal:
HRVData <- BuildNIHR(HRVData)
 
# Filtering heart rate signal:
HRVData <- FilterNIHR(HRVData)

# Interpolating heart rate signal:
HRVData = InterpolateNIHR(HRVData)

HRVData = CreateFreqAnalysis(HRVData)
HRVData = CalculatePowerBand(HRVData, indexFreqAnalysis = 1,
          size = 300, shift = 60, sizesp = 1024)
          
layout(matrix(1:4, nrow = 2))
PlotSinglePowerBand(HRVData, 1, "VLF", Tags = "APNEA", epColorPalette = "red",
                    epLegendCoords = c(2000,7500))
PlotSinglePowerBand(HRVData, 1, "LF", Tags = "APNEA", epColorPalette = "red",
                    eplim = c(0,6000),
                    markEpisodes = F, showEpLegend = FALSE)
PlotSinglePowerBand(HRVData, 1, "HF", Tags = "APNEA", epColorPalette = "red",
                    epLegendCoords = c(2000,1700))
PlotSinglePowerBand(HRVData, 1, "LF/HF", Tags = "APNEA", epColorPalette = "red",
                    eplim = c(0,20),
                    markEpisodes = F, showEpLegend = FALSE)
# Reset layout
par(mfrow = c(1,1))

## End(Not run)

Description

Plots spectrogram of the heart rate signal as calculated by CalculateSpectrogram() function

Usage

PlotSpectrogram(
  HRVData,
  size,
  shift,
  sizesp = NULL,
  freqRange = NULL,
  scale = "linear",
  verbose = NULL,
  showLegend = TRUE,
  Tags = NULL,
  Indexes = NULL,
  eplim = NULL,
  epColorPalette = NULL,
  markEpisodes = TRUE,
  ymark = NULL,
  showEpLegend = TRUE,
  epLegendCoords = NULL,
  main = "Spectrogram of the HR  series",
  xlab = "Time (sec.)",
  ylab = "Frequency (Hz.)",
  ylim = freqRange,
  Tag = NULL,
  ...
)

Arguments

Details

PlotSpectrogram

Note

PlotSpectrogram with showLegend = TRUE uses the layout function and so is restricted to a full page display. Select showLegend = FALSE in order to use the layout function.

Author(s)

M. Lado, A. Mendez, D. Olivieri, L. Rodriguez, X. Vila. C.A. Garcia

References

L. Rodriguez-Linares, A. Mendez, M. Lado, D. Olivieri, X. Vila, I. Gomez-Conde, "An open source tool for heart rate variability spectral analysis", Computer Methods and Programs in Biomedicine 103, 39-50, doi:10.1016/j.cmpb.2010.05.012 (2011)

See Also

CalculateSpectrogram for spectrogram calculation

Examples

## Not run: 

# Read file "a03" from the physionet apnea-ecg database
library(RHRV)
HRVData <- CreateHRVData()
HRVData <- LoadBeatWFDB(HRVData,RecordName="test_files/WFDB/a03")
HRVData <- LoadApneaWFDB(HRVData,RecordName="test_files/WFDB/a03")
# Add other type of episode for a more complete example (this episode does
# not have any physiological meaning)
HRVData <- AddEpisodes(HRVData,InitTimes=c(4500),Durations=c(1000), 
                       Tags="Other", Values = 1)         
# Calculating heart rate signal:
HRVData <- BuildNIHR(HRVData)
 
# Filtering heart rate signal:
HRVData <- FilterNIHR(HRVData)

# Interpolating heart rate signal:
HRVData = InterpolateNIHR(HRVData)
 
# Calculating and Plotting Spectrogram
spctr <- PlotSpectrogram(HRVData, size = 120, shift = 10, sizesp = 1024,
         freqRange=c(0,0.14), color.palette = topo.colors)
         
spctr <- PlotSpectrogram(HRVData,size=120, shift=60, Tags="all", 
                         ylim=c(0,0.1),
                         showLegend=T, 
                         eplim = c(0,0.06),
                         epColorPalette=c("skyblue","white"), 
                         showEpLegend = T,
                         epLegendCoords = c(15000,0.08), 
                         ymark=c(0.001,0.002))

## End(Not run)

Description

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.

Usage

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,
  ...
)

Arguments

Details

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.