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Typos, and general fixes. #7

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2 changes: 2 additions & 0 deletions .gitignore
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@@ -1,4 +1,6 @@
.DS_Store
/Manifest.toml
/docs/Manifest.toml
/dev/
/docs/build/
.Rhistory
8 changes: 4 additions & 4 deletions Project.toml
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@@ -1,7 +1,7 @@
name = "HeartRateVariability"
uuid = "0047d930-76bf-11ea-1377-bb3cda807e9f"
authors = ["Jasmin Walter <jasmin.walter@mni.thm.de>"]
version = "1.0"
version = "1.0.0"

[deps]
Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
Expand All @@ -12,10 +12,10 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
Trapz = "592b5752-818d-11e9-1e9a-2b8ca4a44cd1"

[compat]
Images = "0.22"
LombScargle = "0.5"
StatsBase = "0.33"
Images = "0.22, 0.23"
LombScargle = "0.5, 1.0"
Plots = "1.4"
StatsBase = "0.33, 0.34"
Trapz = "2.0"
julia = "1"

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1 change: 1 addition & 0 deletions docs/Project.toml
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@@ -1,5 +1,6 @@
[deps]
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
HeartRateVariability = "0047d930-76bf-11ea-1377-bb3cda807e9f"

[compat]
Documenter = "0.24"
4 changes: 3 additions & 1 deletion docs/src/introduction.md
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Expand Up @@ -34,7 +34,9 @@ for i=2...n
rr _{i} := \frac{2*(RR_{i}-RR_{i-1})}{RR_{i}+RR_{i-1}}
```
where n is the number of RR intervals.\
The HRV is measured by the median of the euclidean distances of the relative RR intervals to the average of the relative RR intervals. [Read more](https://marcusvollmer.github.io/HRV/files/paper_method.pdf)
The HRV is measured by the median of the euclidean distances of the relative RR intervals to the average of the relative RR intervals. [Read more](https://marcusvollmer.github.io/HRV/files/paper_method.pdf) [^1]

[^1]: Vollmer, M. (2015). A robust, simple and reliable measure of heart rate variability using relative RR intervals. 2015 Computing in Cardiology Conference (CinC), 609–612. https://doi.org/10.1109/CIC.2015.7410984

# Frequency-Domain Analysis

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4 changes: 2 additions & 2 deletions src/Frequency.jl
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Expand Up @@ -5,7 +5,7 @@ import Trapz

#=
This function calculates a lomb scargle transformation
:param n: is the array that contains the NN-inetrvals
:param n: is the array that contains the NN-intervals
:return: the result of the lomb scargle transformation
=#
function lomb_scargle(n)
Expand All @@ -16,7 +16,7 @@ function lomb_scargle(n)
end # lomb_scargle

#=
This function calculates the power of a frequency band between two given frequencys
This function calculates the power of a frequency band between two given frequencies
:param freq: The frequency of a lomb scargle transformation
:param power: The power of a lomb scargle transformation
:param min: The minimum value of the frequency band to be calculated
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6 changes: 3 additions & 3 deletions src/Geometric.jl
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Expand Up @@ -5,7 +5,7 @@ import Images

#=
This function creates a Poincaré plot
:param n: is the array that contains the NN-inetrvals
:param n: is the array that contains the NN-intervals
:return: a plot object
=#
function poincare(n)
Expand All @@ -21,8 +21,8 @@ end # poincare

#=
This function creates a recurrence plot
:param n: is the array that contains the NN-inetrvals
:param e: the maximum distance between two intervals, default="mean" == the mean value of succsessive differences
:param n: is the array that contains the NN-intervals
:param e: the maximum distance between two intervals, default="mean" == the mean value of successive differences
:return: a plot object
=#
function recurrence(n,e)
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12 changes: 6 additions & 6 deletions src/HeartRateVariability.jl
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Expand Up @@ -10,8 +10,8 @@ include("Geometric.jl")
geometric(n,e="mean")

Arguments:
- n: the array that contains the NN-inetrvals
- e: the maximum distance between two intervals, default="mean" (the mean value of the succsessive differences), has to be "mean" or a number
- n: the array that contains the NN-intervals
- e: the maximum distance between two intervals, default="mean" (the mean value of the successive differences), has to be "mean" or a number

Results:
- poincare: the Poincaré plot
Expand All @@ -28,7 +28,7 @@ end # geometric
nonlinear(n,m=2,r=6)

Arguments:
- n: the array that contains the NN-inetrvals
- n: the array that contains the NN-intervals
- m: the embedding dimension, default=2
- r: the tolerance, default=6

Expand All @@ -51,7 +51,7 @@ end # nonlinear
frequency(n)

Arguments:
- n: the array that contains the NN-inetrvals
- n: the array that contains the NN-intervals

Results:
- vlf: the very low-frequency power
Expand All @@ -73,7 +73,7 @@ end # frequency
time_domain(n)

Arguments:
- n: the array that contains the NN-inetrvals
- n: the array that contains the NN-intervals

Results:
- mean: the mean value
Expand Down Expand Up @@ -118,7 +118,7 @@ Arguments:
- annotator: is the annotator of the record

!!! note
In order to use the infile function for wfdb files, the WFDB Software Package from
In order to use the `infile` function for wfdb files, the WFDB Software Package from
Pysionet is required. See [Installation](installation) for more information.
"""
function infile(record::String,annotator::String)
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16 changes: 8 additions & 8 deletions src/Nonlinear.jl
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Expand Up @@ -5,7 +5,7 @@ import Statistics

#=
This function calculates the approximate entropy
:param n: the array that contains the NN-inetrvals
:param n: the array that contains the NN-intervals
:param m: the embedding dimension, default=2
:param r: the tolerance, default=6
:return: the approximate entropy
Expand All @@ -18,7 +18,7 @@ end # apen

#=
This function calculates the sample entropy
:param n: the array that contains the NN-inetrvals
:param n: the array that contains the NN-intervals
:param m: the embedding dimension, default=2
:param r: the tolerance, default=6
:return: the sample entropy
Expand All @@ -31,7 +31,7 @@ end # sampen

#=
This function creates a template of a given array over an embedding dimension
:param n: the array that contains the NN-inetrvals
:param n: the array that contains the NN-intervals
:param m: the embedding dimension, default=2
:return template: the created template
=#
Expand All @@ -45,7 +45,7 @@ end # get_template

#=
This function calculates the distances for the approximate entropy
:param n: the array that contains the NN-inetrvals
:param n: the array that contains the NN-intervals
:param m: the embedding dimension, default=2
:param r: the tolerance, default=6
:return: the distance for the approximate entropy
Expand All @@ -66,7 +66,7 @@ end # get_apen_dist

#=
This function calculates the distances for the sample entropy
:param n: the array that contains the NN-inetrvals
:param n: the array that contains the NN-intervals
:param m: the embedding dimension, default=2
:param r: the tolerance, default=6
:param l: a value to limit the for-loops
Expand All @@ -91,7 +91,7 @@ end # get_sampen_dist

#=
This function calculates the renyi entropy of a given order
:param n: the array that contains the NN-inetrvals
:param n: the array that contains the NN-intervals
:param a: the order of the renyi entropy
:return: the calculated renyi entropy
=#
Expand All @@ -102,7 +102,7 @@ end # renyi
#=
This function calculates the hurst coefficient
It was inspired by the python hurst package by Dmitry A. Mottl (https://github.com/Mottl/hurst)
:param n: the array that contains the NN-inetrvals
:param n: the array that contains the NN-intervals
:return H: the hurst coefficient
=#
function hurst(n)
Expand Down Expand Up @@ -145,7 +145,7 @@ end # hurst
#=
This function calculates the rescaled range of a time series
It was inspired by the python hurst package by Dmitry A. Mottl (https://github.com/Mottl/hurst)
:param n: the array that contains the NN-inetrvals
:param n: the array that contains the NN-intervals
:return: the rescaled range
=#
function get_rs(n)
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8 changes: 4 additions & 4 deletions src/TimeDomain.jl
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Expand Up @@ -4,7 +4,7 @@ import Statistics

#=
This function calculates the differences between the NN intervals
:param n: is the array that contains the NN-inetrvals
:param n: is the array that contains the NN-intervals
:return diff: is an array containing the differences
=#
function nn_diff(n)
Expand All @@ -17,7 +17,7 @@ end #nn_diff

#=
This function calculates the standard deviation of the NN intervals
:param n: is the array that contains the NN-inetrvals
:param n: is the array that contains the NN-intervals
:return: the standard deviation
=#
function sdnn(n)
Expand Down Expand Up @@ -72,7 +72,7 @@ end # nn

#=
This function calculates the mean of the NN intervals
:param n: is the array that contains the NN-inetrvals
:param n: is the array that contains the NN-intervals
:return: the mean value
=#
function mean_nn(n)
Expand All @@ -81,7 +81,7 @@ end # mean_nn

#=
This function calculates the relative RR
:param n: is the array that contains the NN-inetrvals
:param n: is the array that contains the NN-intervals
:return: the relative RR
=#
function rRR(n)
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6 changes: 3 additions & 3 deletions test/runtests.jl
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Expand Up @@ -28,8 +28,8 @@ g=HeartRateVariability.geometric(n)
@testset "HeartRateVariability.frequency" begin
@test fd.vlf≈1317.96 atol=0.01*fd.vlf
@test fd.lf≈90.36 atol=0.01*fd.lf
@test fd.hf≈176.05 atol=0.01*fd.hf
@test fd.lfhf_ratio≈0.51 atol=0.01*fd.lfhf_ratio
@test fd.hf≈186.93 atol=0.01*fd.hf
@test fd.lfhf_ratio≈0.48 atol=0.01*fd.lfhf_ratio
@test fd.tp≈1584.35 atol=0.01*fd.tp
end

Expand All @@ -44,7 +44,7 @@ g=HeartRateVariability.geometric(n)
#testing if get_rs from module Nonlinear returns 0 when S or R is 0
@test HeartRateVariability.Nonlinear.get_rs(ones(100))==0

#testing if warning is thwown
#testing if warning is thrown
@test_logs (:warn,"To obtain a valid value for the hurst coefficient, the length of the data series must be greater than or equal to 100.") HeartRateVariability.nonlinear([1.0,2.0,1.0,2.0,1.0,2.0,1.0,2.0,1.0,2.0])
end

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