Nothing
Automating HRV analysis: RHRVEasy
In RHRV: Heart Rate Variability Analysis of ECG Data
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
RHRVEasy automates all steps of a Heart Rate Variability (HRV) analysis,
including data processing, indices calculation, and statistical analysis. It
takes as input a list of folders, each containing the recordings of a same
population. It calculates time, frequency, and nonlinear domain HRV indices,
and then it applies hypothesis test, and corrects the significance levels. If
there are more than two experimental groups and statistically significant
differences are found, it performs a post-hoc analysis to find out which groups
have the differences.
0. Set up required to run this tutorial
This tutorial uses the recordings of the Normal Sinus Rhythm RR Interval
Database (hereinafter referred to
as NSR_DB), a subset of the RR interval time series from healthy subjects (referred to as HEALTHY_DB), and the Congestive Heart Failure RR Interval
Database
(referred to as CHF_DB). The former two databases comprise data from healthy
individuals, while the latter consists of recordings from patients with severe
cardiac pathology. Consequently, significant disparities in numerous HRV indices
are anticipated between the healthy databases and the CHF_DB.
The three databases are available in the
GitHub repository for the book "Heart Rate Variability Analysis with the R package RHRV", under the data/Chapter8
folder, within the data/Chapter8 directory. To execute this tutorial, download
this folder to your local machine and define the following variables:
library("RHRV")
basePath <- "book_data" # adjust as needed
NSR_DB <- file.path(basePath, "normal")
CHF_DB <- file.path(basePath, "chf")
HEALTHY_DB <- file.path(basePath, "healthy")
RHRVEasy permits creating an Excel spreadsheet with all the HRV indices
calculated for each recording. The following variable must contain the folder
on the local machine where the Excel spreadsheet is to be saved:
spreadsheetPath <- basePath
1. Time and frequency analysis
RHRVEasy enables the user to carry out a full HRV analysis by just invoking a
function with a single mandatory parameter: a list with the folders containing
the recordings of the experimental groups. This list must have at least two
folders. Each folder must contain all the RR recordings of the same
experimental group and no additional files, as RHRVEasy will try to open all
the files within these folders. The name that will be used to refer to each
experimental group within RHRVEasy will be the name of the folder in which its
recordings are located.
The following function call computes the time and frequency indices for the
NSR_DB and CHF_DB databases, and performs a statistical comparison of each
index correcting the significance level with the Bonferroni method. Note the
use of the nJobs to use several cores and parallelize the computations. With
nJobs = -1, it uses all available cores; if an integer greater than 0 is
indicated, it uses the number of cores indicated by the integer.
easyAnalysis <- RHRVEasy(folders = c(NSR_DB, CHF_DB), nJobs = -1)
When the returned object is displayed in the console, it shows which indices
present statistically significant differences:
print(easyAnalysis)
## Significant differences in SDNN (Kruskal-Wallis rank sum test, bonferroni p-value = 1.117154e-07):
## chf's mean95% CI: (61.91503, 94.0085) [Bootstrap CI without adjustment]
## normal's mean95% CI: (131.1187, 148.1985) [Bootstrap CI without adjustment]
##
## Significant differences in SDANN (Kruskal-Wallis rank sum test, bonferroni p-value = 3.799696e-07):
## chf's mean95% CI: (48.19527, 80.0444) [Bootstrap CI without adjustment]
## normal's mean95% CI: (122.0759, 139.05) [Bootstrap CI without adjustment]
##
## Significant differences in SDNNIDX (Kruskal-Wallis rank sum test, bonferroni p-value = 0.01426098):
## chf's mean95% CI: (29.96821, 47.6446) [Bootstrap CI without adjustment]
## normal's mean95% CI: (47.0144, 54.5201) [Bootstrap CI without adjustment]
##
## Significant differences in IRRR (Kruskal-Wallis rank sum test, bonferroni p-value = 1.492754e-07):
## chf's mean95% CI: (78.67064, 124.1918) [Bootstrap CI without adjustment]
## normal's mean95% CI: (189.5291, 215.7118) [Bootstrap CI without adjustment]
##
## Significant differences in TINN (Kruskal-Wallis rank sum test, bonferroni p-value = 1.452872e-06):
## chf's mean95% CI: (243.1949, 373.8965) [Bootstrap CI without adjustment]
## normal's mean95% CI: (511.0544, 586.6332) [Bootstrap CI without adjustment]
##
## Significant differences in HRVi (Kruskal-Wallis rank sum test, bonferroni p-value = 1.452872e-06):
## chf's mean95% CI: (15.96148, 23.78737) [Bootstrap CI without adjustment]
## normal's mean95% CI: (32.80169, 37.58583) [Bootstrap CI without adjustment]
##
## Significant differences in ULF (Kruskal-Wallis rank sum test, bonferroni p-value = 1.74099e-08):
## chf's mean95% CI: (1182.117, 4410.562) [Bootstrap CI without adjustment]
## normal's mean95% CI: (7215.618, 9824.658) [Bootstrap CI without adjustment]
##
## Significant differences in VLF (Kruskal-Wallis rank sum test, bonferroni p-value = 0.002535127):
## chf's mean95% CI: (52.21509, 135.5065) [Bootstrap CI without adjustment]
## normal's mean95% CI: (131.5723, 175.2834) [Bootstrap CI without adjustment]
All computed indices, as well as all p-values resulting from all comparisons,
are stored in data.frames contained in the object. Two different sets of
p-values are available; the ones obtained before (p.value) and after
(adj.p.value) applying the significance level correction:
# HRVIndices
head(easyAnalysis$HRVIndices)
## # A tibble: 6 × 16
## file group SDNN SDANN SDNNIDX pNN50 SDSD rMSSD IRRR MADRR TINN HRVi
## <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 chf201_rr… chf 75.5 52.9 49.6 2.03 20.2 20.2 93.8 7.81 358. 22.9
## 2 chf202_rr… chf 88.5 75.8 39.6 6.13 34.7 34.7 117. 15.6 350. 22.4
## 3 chf203_rr… chf 38.8 30.9 21.7 1.20 17.3 17.3 46.9 7.81 170. 10.9
## 4 chf204_rr… chf 55.1 39.1 36.0 4.84 33.0 33.0 70.3 7.81 237. 15.2
## 5 chf205_rr… chf 34.9 26.1 19.5 1.97 23.7 23.7 46.9 7.81 169. 10.8
## 6 chf206_rr… chf 41.2 34.9 14.8 2.02 18.9 18.9 31.2 7.81 122. 7.79
## # ℹ 4 more variables: ULF <dbl>, VLF <dbl>, LF <dbl>, HF <dbl>
# Statistical analysis
head(easyAnalysis$stats)
## # A tibble: 6 × 4
## HRVIndex method p.value adj.p.value
## <chr> <chr> <dbl> <dbl>
## 1 SDNN Kruskal-Wallis rank sum test 0.00000000798 0.000000112
## 2 SDANN Kruskal-Wallis rank sum test 0.0000000271 0.000000380
## 3 SDNNIDX Kruskal-Wallis rank sum test 0.00102 0.0143
## 4 pNN50 Kruskal-Wallis rank sum test 0.774 1
## 5 SDSD Kruskal-Wallis rank sum test 0.0891 1
## 6 rMSSD Kruskal-Wallis rank sum test 0.0891 1
The format parameter specifies the format in which the RR intervals are
stored. All formats supported by the RHRV package can be used: WFDB, ASCII,
RR, Polar, Suunto, EDFPlus or Ambit (check the RHRV
website for more information). The
default format is RR, where the beat distances in seconds are stored in a
single column of an ASCII file. This is the format of the three databases used
in this tutorial.
By default, the frequency analysis is performed using the Fourier transform. It
is also possible to use the Wavelet transform pasing the value 'wavelet' to
the typeAnalysis parameter (check the paper "García, C. A., Otero, A., Vila,
X., & Márquez, D. G. (2013). A new algorithm for wavelet-based heart rate
variability analysis. Biomedical Signal Processing and Control, 8(6), 542-550"
for details):
easyAnalysisWavelet <- RHRVEasy(
folders = c(NSR_DB, CHF_DB),
typeAnalysis = 'wavelet',
n_jobs = -1
)
Note that the significant indices are the same as the previous ones.
2. Correction of the significance level
Given that multiple statistical tests are performed on several HRV indices, a
correction of the significance level should be applied. The Bonferroni method
is used by default. This behavior can be overridden with
the parameter correctionMethod of RHRVEasy. The possible values of this
parameter besides bonferroni are holm, hochberg, hommel,
BH (Benjamini & Hochberg), fdr (false discovery rate),
BY (Benjamini & Yekutieli), and none (indicating that no correction is to
be made). Furthermore, there is no need to recompute the HRV indices to apply
a different correction method, but the RHRVEasyStats function can be used to
this end. The confidence level can also be changed using the significance
parameter (in both RHRVEasy and RHRVEasyStats functions).
easyAnalysisFDR <- RHRVEasyStats(easyAnalysis, correctionMethod = 'fdr')
pValues <- merge(
easyAnalysis$stats,
easyAnalysisFDR$stats,
by = setdiff(names(easyAnalysis$stats), "adj.p.value"),
suffixes = c(".bonf", ".fdr")
)
#Let us compare the p-values obtained with different correction methods
print(
head(
pValues[, c("HRVIndex", "p.value", "adj.p.value.bonf", "adj.p.value.fdr")]
)
)
## HRVIndex p.value adj.p.value.bonf adj.p.value.fdr
## 1 HF 5.601495e-01 1.000000e+00 6.032380e-01
## 2 HRVi 1.037766e-07 1.452872e-06 2.421454e-07
## 3 IRRR 1.066253e-08 1.492754e-07 4.975847e-08
## 4 LF 1.651479e-02 2.312071e-01 2.568968e-02
## 5 MADRR 6.319903e-02 8.847864e-01 8.847864e-02
## 6 pNN50 7.744691e-01 1.000000e+00 7.744691e-01
3. Saving the indices to an Excel spreadsheet
If the argument saveHRVindicesInPath is specified when invoking the function
RHRVEasy, an Excel spreadsheet with all the HRV indices calculated for each
recording will be created in the path specified by this parameter.
The name of the spreadsheet generated is "Vs
.xlsx":
easyAnalysis <- RHRVEasy(folders = c(NSR_DB, CHF_DB),
saveHRVIndicesInPath = spreadsheetPath)
This spreadsheet can also be generated from the object returned by RHRVEasy
by calling the function SaveHRVIndices.
SaveHRVIndices(easyAnalysis, saveHRVIndicesInPath = spreadsheetPath)
4. Comparing more than two experimental groups
If the analysis involves three or more groups, when statistically significant
differences are found among them it does not necessarily mean that there are
statistically significant differences between all pairs of groups. In such a
scenario post-hoc tests are used to find which pairs of groups present
differences:
#Comparison of the three databases
easyAnalysis3 <- RHRVEasy(
folders = c(NSR_DB, CHF_DB, HEALTHY_DB),
nJobs = -1
)
print(easyAnalysis3)
## Significant differences in SDNN (Kruskal-Wallis rank sum test, bonferroni p-value = 3.543622e-07):
## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment):
## group1 group2 adj.p.value
## 1 healthy chf 0.00799
## 2 normal chf 0.000000282
## ----------------------------
## chf's mean95% CI: (63.20538, 92.2515) [Bootstrap CI without adjustment]
## healthy's mean95% CI: (123.242, 158.269) [Bootstrap CI without adjustment]
## normal's mean95% CI: (131.665, 147.9961) [Bootstrap CI without adjustment]
##
## Significant differences in SDANN (Kruskal-Wallis rank sum test, bonferroni p-value = 1.345688e-06):
## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment):
## group1 group2 adj.p.value
## 1 normal chf 0.000000403
## ---------------------------
## chf's mean95% CI: (47.61222, 81.42191) [Bootstrap CI without adjustment]
## healthy's mean95% CI: (105.1872, 134.0331) [Bootstrap CI without adjustment]
## normal's mean95% CI: (120.4753, 138.5329) [Bootstrap CI without adjustment]
##
## Significant differences in SDNNIDX (Kruskal-Wallis rank sum test, bonferroni p-value = 0.001063849):
## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment):
## group1 group2 adj.p.value
## 1 healthy chf 0.00111
## ----------------------------
## chf's mean95% CI: (29.1345, 47.73994) [Bootstrap CI without adjustment]
## healthy's mean95% CI: (56.23389, 74.9991) [Bootstrap CI without adjustment]
## normal's mean95% CI: (47.0101, 54.33106) [Bootstrap CI without adjustment]
##
## Significant differences in IRRR (Kruskal-Wallis rank sum test, bonferroni p-value = 3.688167e-07):
## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment):
## group1 group2 adj.p.value
## 1 healthy chf 0.00395
## 2 normal chf 0.000000425
## ----------------------------
## chf's mean95% CI: (77.3305, 124.7238) [Bootstrap CI without adjustment]
## healthy's mean95% CI: (179.9086, 234.5556) [Bootstrap CI without adjustment]
## normal's mean95% CI: (187.6484, 215.9975) [Bootstrap CI without adjustment]
##
## Significant differences in MADRR (Kruskal-Wallis rank sum test, bonferroni p-value = 0.006224158):
## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment):
## group1 group2 adj.p.value
## 1 healthy chf 0.00237
## ----------------------------
## chf's mean95% CI: (8.62069, 11.85345) [Bootstrap CI without adjustment]
## healthy's mean95% CI: (16.55556, 24.66667) [Bootstrap CI without adjustment]
## normal's mean95% CI: (11.28472, 14.03356) [Bootstrap CI without adjustment]
##
## Significant differences in TINN (Kruskal-Wallis rank sum test, bonferroni p-value = 1.350844e-06):
## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment):
## group1 group2 adj.p.value
## 1 healthy chf 0.000933
## 2 normal chf 0.00000519
## ----------------------------
## chf's mean95% CI: (244.0477, 371.3618) [Bootstrap CI without adjustment]
## healthy's mean95% CI: (533.6798, 701.4795) [Bootstrap CI without adjustment]
## normal's mean95% CI: (511.6379, 586.4394) [Bootstrap CI without adjustment]
##
## Significant differences in HRVi (Kruskal-Wallis rank sum test, bonferroni p-value = 1.350844e-06):
## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment):
## group1 group2 adj.p.value
## 1 healthy chf 0.000933
## 2 normal chf 0.00000519
## ----------------------------
## chf's mean95% CI: (15.85798, 23.7487) [Bootstrap CI without adjustment]
## healthy's mean95% CI: (34.45, 45.19331) [Bootstrap CI without adjustment]
## normal's mean95% CI: (32.68737, 37.61479) [Bootstrap CI without adjustment]
##
## Significant differences in ULF (Kruskal-Wallis rank sum test, bonferroni p-value = 5.860632e-08):
## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment):
## group1 group2 adj.p.value
## 1 normal chf 0.0000000162
## ----------------------------
## chf's mean95% CI: (1075.296, 4358.885) [Bootstrap CI without adjustment]
## healthy's mean95% CI: (4995.594, 8167.694) [Bootstrap CI without adjustment]
## normal's mean95% CI: (7063.468, 9898.164) [Bootstrap CI without adjustment]
##
## Significant differences in VLF (Kruskal-Wallis rank sum test, bonferroni p-value = 0.0005669878):
## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment):
## group1 group2 adj.p.value
## 1 healthy chf 0.00239
## 2 normal chf 0.00977
## ----------------------------
## chf's mean95% CI: (54.04686, 134.9712) [Bootstrap CI without adjustment]
## healthy's mean95% CI: (171.6335, 340.8925) [Bootstrap CI without adjustment]
## normal's mean95% CI: (130.0847, 177.0061) [Bootstrap CI without adjustment]
Note that the stats data.frame now contains a column named pairwise storing
the results of the post-hoc analysis for those indices where the omnibus test
has been significant:
print(head(easyAnalysis3$stats))
## # A tibble: 6 × 5
## HRVIndex method p.value adj.p.value pairwise
## <chr> <chr> <dbl> <dbl> <list>
## 1 SDNN Kruskal-Wallis rank sum test 0.0000000253 0.000000354 <tibble>
## 2 SDANN Kruskal-Wallis rank sum test 0.0000000961 0.00000135 <tibble>
## 3 SDNNIDX Kruskal-Wallis rank sum test 0.0000760 0.00106 <tibble>
## 4 pNN50 Kruskal-Wallis rank sum test 0.0186 0.260 <NULL>
## 5 SDSD Kruskal-Wallis rank sum test 0.0301 0.421 <NULL>
## 6 rMSSD Kruskal-Wallis rank sum test 0.0301 0.421 <NULL>
# Let's print the post-hoc comparisons for "SDNN"
print(head(easyAnalysis3$stats$pairwise[[1]]))
## # A tibble: 3 × 6
## HRVIndex group1 group2 method p.value adj.p.value
## <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 SDNN healthy chf Dunn's all-pairs test 0.000296 0.00799
## 2 SDNN normal chf Dunn's all-pairs test 0.0000000104 0.000000282
## 3 SDNN normal healthy Dunn's all-pairs test 0.861 1
5. Overwriting default parameters
Any parameter of any RHRV function can be specified as an additional parameter
of the RHRVEasy function; in this case, the default value used for that
parameter will be overwritten by the one specified for the user. The default
values used in the RHRVEasy package are the same as those used in the RHRV
package. For more information about the parameters available you can consult
the RHRV website. For example, the
following analysis modifies the the limits of the ULF, VLF, LF and HF spectral
bands, and uses an interpolation frequency (freqhr) of 2 Hz:
easyAnalysisOverwritten <- RHRVEasy(folders = c(NSR_DB, CHF_DB),
freqhr = 2,
ULFmin = 0, ULFmax = 0.02,
VLFmin = 0.02, VLFmax = 0.07,
LFmin = 0.07, LFmax = 0.20,
HFmin = 0.20, HFmax = 0.5)
6. Nonlinear analysis
The calculation of the nonlinear indices requires considerable computational
resources, specially the Recurrence Quantification Analysis (RQA).
Whereas in a typical HRV analysis the computation of all the time
and frequency domain indices for a few dozens of recordings often completes
within a few minutes, the computation of the nonlinear indices could last many
hours. That's why the boolean parameters nonLinear and doRQA are set to FALSE
by default. If these parameters are not changed, only time and frequency indices
will be calculated, as in the previous sections.
Warning: the following sentence, will take several hours to execute on a
medium to high performance PC. You may reproduce the results of the paper by
running this chunk of code.
fullAnalysis <- RHRVEasy(
folders = c(NSR_DB, CHF_DB, HEALTHY_DB),
nJobs = -1,
nonLinear = TRUE,
doRQA = TRUE,
saveHRVIndicesInPath = spreadsheetPath
)
Try the RHRV package in your browser
Any scripts or data that you put into this service are public.
RHRV documentation built on Oct. 1, 2024, 1:08 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(warning = FALSE, message = FALSE)
RHRVEasy automates all steps of a Heart Rate Variability (HRV) analysis, including data processing, indices calculation, and statistical analysis. It takes as input a list of folders, each containing the recordings of a same population. It calculates time, frequency, and nonlinear domain HRV indices, and then it applies hypothesis test, and corrects the significance levels. If there are more than two experimental groups and statistically significant differences are found, it performs a post-hoc analysis to find out which groups have the differences.
0. Set up required to run this tutorial
This tutorial uses the recordings of the Normal Sinus Rhythm RR Interval Database (hereinafter referred to as NSR_DB), a subset of the RR interval time series from healthy subjects (referred to as HEALTHY_DB), and the Congestive Heart Failure RR Interval Database (referred to as CHF_DB). The former two databases comprise data from healthy individuals, while the latter consists of recordings from patients with severe cardiac pathology. Consequently, significant disparities in numerous HRV indices are anticipated between the healthy databases and the CHF_DB.
The three databases are available in the
GitHub repository for the book "Heart Rate Variability Analysis with the R package RHRV", under the data/Chapter8
folder, within the data/Chapter8 directory. To execute this tutorial, download
this folder to your local machine and define the following variables:
library("RHRV") basePath <- "book_data" # adjust as needed NSR_DB <- file.path(basePath, "normal") CHF_DB <- file.path(basePath, "chf") HEALTHY_DB <- file.path(basePath, "healthy")
RHRVEasy permits creating an Excel spreadsheet with all the HRV indices calculated for each recording. The following variable must contain the folder on the local machine where the Excel spreadsheet is to be saved:
spreadsheetPath <- basePath
1. Time and frequency analysis
RHRVEasy enables the user to carry out a full HRV analysis by just invoking a
function with a single mandatory parameter: a list with the folders containing
the recordings of the experimental groups. This list must have at least two
folders. Each folder must contain all the RR recordings of the same
experimental group and no additional files, as RHRVEasy will try to open all
the files within these folders. The name that will be used to refer to each
experimental group within RHRVEasy will be the name of the folder in which its
recordings are located.
The following function call computes the time and frequency indices for the
NSR_DB and CHF_DB databases, and performs a statistical comparison of each
index correcting the significance level with the Bonferroni method. Note the
use of the nJobs to use several cores and parallelize the computations. With
nJobs = -1, it uses all available cores; if an integer greater than 0 is
indicated, it uses the number of cores indicated by the integer.
easyAnalysis <- RHRVEasy(folders = c(NSR_DB, CHF_DB), nJobs = -1)
When the returned object is displayed in the console, it shows which indices present statistically significant differences:
print(easyAnalysis)
## Significant differences in SDNN (Kruskal-Wallis rank sum test, bonferroni p-value = 1.117154e-07): ## chf's mean95% CI: (61.91503, 94.0085) [Bootstrap CI without adjustment] ## normal's mean95% CI: (131.1187, 148.1985) [Bootstrap CI without adjustment] ## ## Significant differences in SDANN (Kruskal-Wallis rank sum test, bonferroni p-value = 3.799696e-07): ## chf's mean95% CI: (48.19527, 80.0444) [Bootstrap CI without adjustment] ## normal's mean95% CI: (122.0759, 139.05) [Bootstrap CI without adjustment] ## ## Significant differences in SDNNIDX (Kruskal-Wallis rank sum test, bonferroni p-value = 0.01426098): ## chf's mean95% CI: (29.96821, 47.6446) [Bootstrap CI without adjustment] ## normal's mean95% CI: (47.0144, 54.5201) [Bootstrap CI without adjustment] ## ## Significant differences in IRRR (Kruskal-Wallis rank sum test, bonferroni p-value = 1.492754e-07): ## chf's mean95% CI: (78.67064, 124.1918) [Bootstrap CI without adjustment] ## normal's mean95% CI: (189.5291, 215.7118) [Bootstrap CI without adjustment] ## ## Significant differences in TINN (Kruskal-Wallis rank sum test, bonferroni p-value = 1.452872e-06): ## chf's mean95% CI: (243.1949, 373.8965) [Bootstrap CI without adjustment] ## normal's mean95% CI: (511.0544, 586.6332) [Bootstrap CI without adjustment] ## ## Significant differences in HRVi (Kruskal-Wallis rank sum test, bonferroni p-value = 1.452872e-06): ## chf's mean95% CI: (15.96148, 23.78737) [Bootstrap CI without adjustment] ## normal's mean95% CI: (32.80169, 37.58583) [Bootstrap CI without adjustment] ## ## Significant differences in ULF (Kruskal-Wallis rank sum test, bonferroni p-value = 1.74099e-08): ## chf's mean95% CI: (1182.117, 4410.562) [Bootstrap CI without adjustment] ## normal's mean95% CI: (7215.618, 9824.658) [Bootstrap CI without adjustment] ## ## Significant differences in VLF (Kruskal-Wallis rank sum test, bonferroni p-value = 0.002535127): ## chf's mean95% CI: (52.21509, 135.5065) [Bootstrap CI without adjustment] ## normal's mean95% CI: (131.5723, 175.2834) [Bootstrap CI without adjustment]
All computed indices, as well as all p-values resulting from all comparisons,
are stored in data.frames contained in the object. Two different sets of
p-values are available; the ones obtained before (p.value) and after
(adj.p.value) applying the significance level correction:
# HRVIndices head(easyAnalysis$HRVIndices)
## # A tibble: 6 × 16 ## file group SDNN SDANN SDNNIDX pNN50 SDSD rMSSD IRRR MADRR TINN HRVi ## <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 chf201_rr… chf 75.5 52.9 49.6 2.03 20.2 20.2 93.8 7.81 358. 22.9 ## 2 chf202_rr… chf 88.5 75.8 39.6 6.13 34.7 34.7 117. 15.6 350. 22.4 ## 3 chf203_rr… chf 38.8 30.9 21.7 1.20 17.3 17.3 46.9 7.81 170. 10.9 ## 4 chf204_rr… chf 55.1 39.1 36.0 4.84 33.0 33.0 70.3 7.81 237. 15.2 ## 5 chf205_rr… chf 34.9 26.1 19.5 1.97 23.7 23.7 46.9 7.81 169. 10.8 ## 6 chf206_rr… chf 41.2 34.9 14.8 2.02 18.9 18.9 31.2 7.81 122. 7.79 ## # ℹ 4 more variables: ULF <dbl>, VLF <dbl>, LF <dbl>, HF <dbl>
# Statistical analysis head(easyAnalysis$stats)
## # A tibble: 6 × 4 ## HRVIndex method p.value adj.p.value ## <chr> <chr> <dbl> <dbl> ## 1 SDNN Kruskal-Wallis rank sum test 0.00000000798 0.000000112 ## 2 SDANN Kruskal-Wallis rank sum test 0.0000000271 0.000000380 ## 3 SDNNIDX Kruskal-Wallis rank sum test 0.00102 0.0143 ## 4 pNN50 Kruskal-Wallis rank sum test 0.774 1 ## 5 SDSD Kruskal-Wallis rank sum test 0.0891 1 ## 6 rMSSD Kruskal-Wallis rank sum test 0.0891 1
The format parameter specifies the format in which the RR intervals are
stored. All formats supported by the RHRV package can be used: WFDB, ASCII,
RR, Polar, Suunto, EDFPlus or Ambit (check the RHRV
website for more information). The
default format is RR, where the beat distances in seconds are stored in a
single column of an ASCII file. This is the format of the three databases used
in this tutorial.
By default, the frequency analysis is performed using the Fourier transform. It
is also possible to use the Wavelet transform pasing the value 'wavelet' to
the typeAnalysis parameter (check the paper "García, C. A., Otero, A., Vila,
X., & Márquez, D. G. (2013). A new algorithm for wavelet-based heart rate
variability analysis. Biomedical Signal Processing and Control, 8(6), 542-550"
for details):
easyAnalysisWavelet <- RHRVEasy( folders = c(NSR_DB, CHF_DB), typeAnalysis = 'wavelet', n_jobs = -1 )
Note that the significant indices are the same as the previous ones.
2. Correction of the significance level
Given that multiple statistical tests are performed on several HRV indices, a
correction of the significance level should be applied. The Bonferroni method
is used by default. This behavior can be overridden with
the parameter correctionMethod of RHRVEasy. The possible values of this
parameter besides bonferroni are holm, hochberg, hommel,
BH (Benjamini & Hochberg), fdr (false discovery rate),
BY (Benjamini & Yekutieli), and none (indicating that no correction is to
be made). Furthermore, there is no need to recompute the HRV indices to apply
a different correction method, but the RHRVEasyStats function can be used to
this end. The confidence level can also be changed using the significance
parameter (in both RHRVEasy and RHRVEasyStats functions).
easyAnalysisFDR <- RHRVEasyStats(easyAnalysis, correctionMethod = 'fdr') pValues <- merge( easyAnalysis$stats, easyAnalysisFDR$stats, by = setdiff(names(easyAnalysis$stats), "adj.p.value"), suffixes = c(".bonf", ".fdr") ) #Let us compare the p-values obtained with different correction methods print( head( pValues[, c("HRVIndex", "p.value", "adj.p.value.bonf", "adj.p.value.fdr")] ) )
## HRVIndex p.value adj.p.value.bonf adj.p.value.fdr ## 1 HF 5.601495e-01 1.000000e+00 6.032380e-01 ## 2 HRVi 1.037766e-07 1.452872e-06 2.421454e-07 ## 3 IRRR 1.066253e-08 1.492754e-07 4.975847e-08 ## 4 LF 1.651479e-02 2.312071e-01 2.568968e-02 ## 5 MADRR 6.319903e-02 8.847864e-01 8.847864e-02 ## 6 pNN50 7.744691e-01 1.000000e+00 7.744691e-01
3. Saving the indices to an Excel spreadsheet
If the argument saveHRVindicesInPath is specified when invoking the function
RHRVEasy, an Excel spreadsheet with all the HRV indices calculated for each
recording will be created in the path specified by this parameter.
The name of the spreadsheet generated is "
easyAnalysis <- RHRVEasy(folders = c(NSR_DB, CHF_DB), saveHRVIndicesInPath = spreadsheetPath)
This spreadsheet can also be generated from the object returned by RHRVEasy
by calling the function SaveHRVIndices.
SaveHRVIndices(easyAnalysis, saveHRVIndicesInPath = spreadsheetPath)
4. Comparing more than two experimental groups
If the analysis involves three or more groups, when statistically significant differences are found among them it does not necessarily mean that there are statistically significant differences between all pairs of groups. In such a scenario post-hoc tests are used to find which pairs of groups present differences:
#Comparison of the three databases easyAnalysis3 <- RHRVEasy( folders = c(NSR_DB, CHF_DB, HEALTHY_DB), nJobs = -1 ) print(easyAnalysis3)
## Significant differences in SDNN (Kruskal-Wallis rank sum test, bonferroni p-value = 3.543622e-07): ## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment): ## group1 group2 adj.p.value ## 1 healthy chf 0.00799 ## 2 normal chf 0.000000282 ## ---------------------------- ## chf's mean95% CI: (63.20538, 92.2515) [Bootstrap CI without adjustment] ## healthy's mean95% CI: (123.242, 158.269) [Bootstrap CI without adjustment] ## normal's mean95% CI: (131.665, 147.9961) [Bootstrap CI without adjustment] ## ## Significant differences in SDANN (Kruskal-Wallis rank sum test, bonferroni p-value = 1.345688e-06): ## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment): ## group1 group2 adj.p.value ## 1 normal chf 0.000000403 ## --------------------------- ## chf's mean95% CI: (47.61222, 81.42191) [Bootstrap CI without adjustment] ## healthy's mean95% CI: (105.1872, 134.0331) [Bootstrap CI without adjustment] ## normal's mean95% CI: (120.4753, 138.5329) [Bootstrap CI without adjustment] ## ## Significant differences in SDNNIDX (Kruskal-Wallis rank sum test, bonferroni p-value = 0.001063849): ## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment): ## group1 group2 adj.p.value ## 1 healthy chf 0.00111 ## ---------------------------- ## chf's mean95% CI: (29.1345, 47.73994) [Bootstrap CI without adjustment] ## healthy's mean95% CI: (56.23389, 74.9991) [Bootstrap CI without adjustment] ## normal's mean95% CI: (47.0101, 54.33106) [Bootstrap CI without adjustment] ## ## Significant differences in IRRR (Kruskal-Wallis rank sum test, bonferroni p-value = 3.688167e-07): ## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment): ## group1 group2 adj.p.value ## 1 healthy chf 0.00395 ## 2 normal chf 0.000000425 ## ---------------------------- ## chf's mean95% CI: (77.3305, 124.7238) [Bootstrap CI without adjustment] ## healthy's mean95% CI: (179.9086, 234.5556) [Bootstrap CI without adjustment] ## normal's mean95% CI: (187.6484, 215.9975) [Bootstrap CI without adjustment] ## ## Significant differences in MADRR (Kruskal-Wallis rank sum test, bonferroni p-value = 0.006224158): ## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment): ## group1 group2 adj.p.value ## 1 healthy chf 0.00237 ## ---------------------------- ## chf's mean95% CI: (8.62069, 11.85345) [Bootstrap CI without adjustment] ## healthy's mean95% CI: (16.55556, 24.66667) [Bootstrap CI without adjustment] ## normal's mean95% CI: (11.28472, 14.03356) [Bootstrap CI without adjustment] ## ## Significant differences in TINN (Kruskal-Wallis rank sum test, bonferroni p-value = 1.350844e-06): ## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment): ## group1 group2 adj.p.value ## 1 healthy chf 0.000933 ## 2 normal chf 0.00000519 ## ---------------------------- ## chf's mean95% CI: (244.0477, 371.3618) [Bootstrap CI without adjustment] ## healthy's mean95% CI: (533.6798, 701.4795) [Bootstrap CI without adjustment] ## normal's mean95% CI: (511.6379, 586.4394) [Bootstrap CI without adjustment] ## ## Significant differences in HRVi (Kruskal-Wallis rank sum test, bonferroni p-value = 1.350844e-06): ## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment): ## group1 group2 adj.p.value ## 1 healthy chf 0.000933 ## 2 normal chf 0.00000519 ## ---------------------------- ## chf's mean95% CI: (15.85798, 23.7487) [Bootstrap CI without adjustment] ## healthy's mean95% CI: (34.45, 45.19331) [Bootstrap CI without adjustment] ## normal's mean95% CI: (32.68737, 37.61479) [Bootstrap CI without adjustment] ## ## Significant differences in ULF (Kruskal-Wallis rank sum test, bonferroni p-value = 5.860632e-08): ## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment): ## group1 group2 adj.p.value ## 1 normal chf 0.0000000162 ## ---------------------------- ## chf's mean95% CI: (1075.296, 4358.885) [Bootstrap CI without adjustment] ## healthy's mean95% CI: (4995.594, 8167.694) [Bootstrap CI without adjustment] ## normal's mean95% CI: (7063.468, 9898.164) [Bootstrap CI without adjustment] ## ## Significant differences in VLF (Kruskal-Wallis rank sum test, bonferroni p-value = 0.0005669878): ## Significant differences in the post-hoc tests (Dunn's all-pairs test + bonferroni-p-value adjustment): ## group1 group2 adj.p.value ## 1 healthy chf 0.00239 ## 2 normal chf 0.00977 ## ---------------------------- ## chf's mean95% CI: (54.04686, 134.9712) [Bootstrap CI without adjustment] ## healthy's mean95% CI: (171.6335, 340.8925) [Bootstrap CI without adjustment] ## normal's mean95% CI: (130.0847, 177.0061) [Bootstrap CI without adjustment]
Note that the stats data.frame now contains a column named pairwise storing
the results of the post-hoc analysis for those indices where the omnibus test
has been significant:
print(head(easyAnalysis3$stats))
## # A tibble: 6 × 5 ## HRVIndex method p.value adj.p.value pairwise ## <chr> <chr> <dbl> <dbl> <list> ## 1 SDNN Kruskal-Wallis rank sum test 0.0000000253 0.000000354 <tibble> ## 2 SDANN Kruskal-Wallis rank sum test 0.0000000961 0.00000135 <tibble> ## 3 SDNNIDX Kruskal-Wallis rank sum test 0.0000760 0.00106 <tibble> ## 4 pNN50 Kruskal-Wallis rank sum test 0.0186 0.260 <NULL> ## 5 SDSD Kruskal-Wallis rank sum test 0.0301 0.421 <NULL> ## 6 rMSSD Kruskal-Wallis rank sum test 0.0301 0.421 <NULL>
# Let's print the post-hoc comparisons for "SDNN" print(head(easyAnalysis3$stats$pairwise[[1]]))
## # A tibble: 3 × 6 ## HRVIndex group1 group2 method p.value adj.p.value ## <chr> <chr> <chr> <chr> <dbl> <dbl> ## 1 SDNN healthy chf Dunn's all-pairs test 0.000296 0.00799 ## 2 SDNN normal chf Dunn's all-pairs test 0.0000000104 0.000000282 ## 3 SDNN normal healthy Dunn's all-pairs test 0.861 1
5. Overwriting default parameters
Any parameter of any RHRV function can be specified as an additional parameter
of the RHRVEasy function; in this case, the default value used for that
parameter will be overwritten by the one specified for the user. The default
values used in the RHRVEasy package are the same as those used in the RHRV
package. For more information about the parameters available you can consult
the RHRV website. For example, the
following analysis modifies the the limits of the ULF, VLF, LF and HF spectral
bands, and uses an interpolation frequency (freqhr) of 2 Hz:
easyAnalysisOverwritten <- RHRVEasy(folders = c(NSR_DB, CHF_DB), freqhr = 2, ULFmin = 0, ULFmax = 0.02, VLFmin = 0.02, VLFmax = 0.07, LFmin = 0.07, LFmax = 0.20, HFmin = 0.20, HFmax = 0.5)
6. Nonlinear analysis
The calculation of the nonlinear indices requires considerable computational
resources, specially the Recurrence Quantification Analysis (RQA).
Whereas in a typical HRV analysis the computation of all the time
and frequency domain indices for a few dozens of recordings often completes
within a few minutes, the computation of the nonlinear indices could last many
hours. That's why the boolean parameters nonLinear and doRQA are set to FALSE
by default. If these parameters are not changed, only time and frequency indices
will be calculated, as in the previous sections.
Warning: the following sentence, will take several hours to execute on a medium to high performance PC. You may reproduce the results of the paper by running this chunk of code.
fullAnalysis <- RHRVEasy( folders = c(NSR_DB, CHF_DB, HEALTHY_DB), nJobs = -1, nonLinear = TRUE, doRQA = TRUE, saveHRVIndicesInPath = spreadsheetPath )
Try the RHRV package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.