| Title: | Analysis Functions of Respiratory Data |
|---|---|
| Description: | Provides functions for the complete analysis of respiratory data. Consists of a set of functions that allow to preprocessing respiratory data, calculate both regular statistics and nonlinear statistics, conduct group comparison and visualize the results. Especially, Power Spectral Density ('PSD') (A. Eke (2000) <doi:10.1007/s004249900135>), 'MultiScale Entropy(MSE)' ('Madalena Costa(2002)' <doi:10.1103/PhysRevLett.89.068102>) and 'MultiFractal Detrended Fluctuation Analysis(MFDFA)' ('Jan W.Kantelhardt' (2002) <doi:10.1016/S0378-4371(02)01383-3>) were applied for the analysis of respiratory data. |
| Authors: | Xiaohua Douglas Zhang [aut, cph], Teng Zhang [aut], Xinzheng Dong [aut, cre] |
| Maintainer: | Xinzheng Dong <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0.2 |
| Built: | 2024-11-07 03:56:16 UTC |
| Source: | https://github.com/dongxinzheng/respiranalyzer |
Provides functions for the complete analysis of respiratory data. Consists of a set of functions that allow to preprocessing respiratory data, calculate both regular statistics and nonlinear statistics, conduct group comparison and visualize the results. Especially, Power Spectral Density ('PSD') (A. Eke (2000) <doi:10.1007/s004249900135>), 'MultiScale Entropy(MSE)' ('Madalena Costa(2002)' <doi:10.1103/PhysRevLett.89.068102>) and 'MultiFractal Detrended Fluctuation Analysis(MFDFA)' ('Jan W.Kantelhardt' (2002) <doi:10.1016/S0378-4371(02)01383-3>) were applied for the analysis of respiratory data.
The R package RespirAnalyzer contains functions for analyzing respiratory data.
Xiaohua Douglas Zhang [aut, cph], Teng Zhang [aut], Xinzheng Dong [aut, cre]
Maintainer: Xinzheng Dong <[email protected]>
Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals.
# load Data from TestData dataset. The TestData is the respiratory data obtained from a healthy people which can download from the Fantasia database (PhysioNet, https://physionet.org/content/fantasia/1.0.0/) data("TestData") Seriesplot.fn(Data[1:2000,1],Data[1:2000,2],points=FALSE, xlab="Time(s)",ylab="Respiratory") Fs=50 ## sampling frequency of the TestData is 50Hz #### find the peaks of the TestData Peaks <- find.peaks(Data[,2],Fs,lowpass=TRUE,freq=1,MovingAv=FALSE, W=FALSE,filter=TRUE,threshold=0.05) points(Data[Peaks[2:13,1],1],Data[Peaks[2:13,1],2],col=2) #### caculate the peak-to-peak intervals of the TestData PP_interval <- diff(Peaks[,1])/Fs Seriesplot.fn(1:length(PP_interval),PP_interval,points=FALSE,xlab="Count", ylab="Inter-breath Interval(s)") #### Smoothing the series by Moving Average or Low pass filter can make the peaks to be found more precise #### Moving Average and Low pass filter are the argument in the function of find.peaks #### calculating Moving Average of a series W <- FS <- 50 Data[,3] <- MovingAverage(Data[,2],W) Seriesplot.fn(Data[1:2000,1],Data[1:2000,2],points=FALSE, xlab="Time(s)",ylab="Respiratory") lines(Data[1:2000,1],Data[1:2000,3],col=2) #### Low pass filter of a series bf <- signal::butter(2, 2/Fs, type="low") Data[,4] <- signal::filtfilt(bf,Data[,2]) Seriesplot.fn(Data[1:2000,1],Data[1:2000,2],points=FALSE, xlab="Time(s)",ylab="Respiratory") lines(Data[1:2000,1],Data[1:2000,4],col=2) #### calcualte the multiscale entropy of rawdata scale_raw <- seq(1,90,2) MSE <- MSE(Data$V2[seq(1,100000,2)], tau=scale_raw, m=2, r=0.15, I=40000) Seriesplot.fn(MSE$tau ,MSE$SampEn,points=TRUE, xlab="Scale",ylab="Sample entropy") #### calcualate the multiscale entropy of peak-to-peak intervals scale_PP <- 1:10 # setting the scale of entropy MSE <- MSE(PP_interval, tau=scale_PP, m=2, r=0.15, I=40000) Seriesplot.fn(MSE$tau ,MSE$SampEn,points=TRUE, xlab="Scale",ylab="Sample entropy") #### Power Spectral Density (PSD) analysis of the peak-to-peak intervals LowPSD(PP_interval, plot=TRUE,min=1/64, max=1/2) #### MultiFractal Detrended Fluctuation Analysis of the peak-to-peak intervals exponents=seq(3, 9, by=1/4) scale=2^exponents #Vector of scales q=-10:10 #q-order of the moment m=2 #An integer of the polynomial order for the detrending Result <- MFDFA(PP_interval, scale, m, q) MFDFAplot.fn(Result,scale,q,model = TRUE) #### fit the MFDFA result with the extended binomial multifractal model Coeff <- fit.model(Result$Hq,q) Coeff #### Caculate the parameter of multifractal model Para<- -log(Coeff)/log(2);Para[3]=Para[1]-Para[2] names(Para)<-c("Hmax","Hmin","i÷H") Para #### plot MFDFA results by individual data("HqData") PP_Hq <- HqData filenames <- row.names(PP_Hq) q=-10:10 # define the range of q-order of the moment ClassNames <- c(substr(filenames[1:19], start = 1, stop = 3), substr(filenames[20:38], start = 1, stop = 5)) Class <- unique(ClassNames) # Obtain group name col_vec <- rep(NA, nrow(PP_Hq) ) pch_vec <- rep(16, nrow(PP_Hq) ) for( i in 1:length(Class) ) { col_vec[ ClassNames == Class[i] ] <- i } Individualplot.fn(q,PP_Hq,Name=Class,col=col_vec,pch=pch_vec, xlab="q",ylab="Hurst exponent") legend("topright", legend=paste0(Class, "(N=", table( ClassNames ), ")"), col=1:4, cex=1, lty=1, pch=16) #### plot the mean and error bar of by MFDFA results group data("HqData") PP_Hq <- HqData filenames <- row.names(PP_Hq) q <- -10:10 # define the range of q-order of the moment ClassNames <- c(substr(filenames[1:19], start = 1, stop = 3), substr(filenames[20:38], start = 1, stop = 5)) Class <- unique(ClassNames) for (i in 1:length(q)){ Data <- GroupComparison.fn(PP_Hq[,i],ClassNames) Result_mean_vec <- Data[,"Mean"] Result_sd_vec <- Data[,"SE"] if( i == 1 ) { Result_mean_mat <- Result_mean_vec Result_sd_mat <- Result_sd_vec } else { Result_mean_mat <- rbind(Result_mean_mat, Result_mean_vec) Result_sd_mat <- rbind(Result_sd_mat, Result_sd_vec) } } #### Hurst exponent of q 0-10 Groupplot.fn (q[1:10],Result_mean_mat[1:10,],Class,errorbar = Result_sd_mat[1:10,], xRange = NA, yRange = NA, col = NA, pch = rep(16,4), Position = "topright", cex.legend = 1, xlab="q",ylab="Hurst exponent",main = "") #### Hurst exponent of q -10-0 Groupplot.fn (q[11:21],Result_mean_mat[11:21,],Class,errorbar = Result_sd_mat[11:21,], xRange = NA, yRange = NA, col = NA, pch = rep(16,4), Position = "topright", cex.legend = 1, xlab="q",ylab="Hurst exponent",main = "")# load Data from TestData dataset. The TestData is the respiratory data obtained from a healthy people which can download from the Fantasia database (PhysioNet, https://physionet.org/content/fantasia/1.0.0/) data("TestData") Seriesplot.fn(Data[1:2000,1],Data[1:2000,2],points=FALSE, xlab="Time(s)",ylab="Respiratory") Fs=50 ## sampling frequency of the TestData is 50Hz #### find the peaks of the TestData Peaks <- find.peaks(Data[,2],Fs,lowpass=TRUE,freq=1,MovingAv=FALSE, W=FALSE,filter=TRUE,threshold=0.05) points(Data[Peaks[2:13,1],1],Data[Peaks[2:13,1],2],col=2) #### caculate the peak-to-peak intervals of the TestData PP_interval <- diff(Peaks[,1])/Fs Seriesplot.fn(1:length(PP_interval),PP_interval,points=FALSE,xlab="Count", ylab="Inter-breath Interval(s)") #### Smoothing the series by Moving Average or Low pass filter can make the peaks to be found more precise #### Moving Average and Low pass filter are the argument in the function of find.peaks #### calculating Moving Average of a series W <- FS <- 50 Data[,3] <- MovingAverage(Data[,2],W) Seriesplot.fn(Data[1:2000,1],Data[1:2000,2],points=FALSE, xlab="Time(s)",ylab="Respiratory") lines(Data[1:2000,1],Data[1:2000,3],col=2) #### Low pass filter of a series bf <- signal::butter(2, 2/Fs, type="low") Data[,4] <- signal::filtfilt(bf,Data[,2]) Seriesplot.fn(Data[1:2000,1],Data[1:2000,2],points=FALSE, xlab="Time(s)",ylab="Respiratory") lines(Data[1:2000,1],Data[1:2000,4],col=2) #### calcualte the multiscale entropy of rawdata scale_raw <- seq(1,90,2) MSE <- MSE(Data$V2[seq(1,100000,2)], tau=scale_raw, m=2, r=0.15, I=40000) Seriesplot.fn(MSE$tau ,MSE$SampEn,points=TRUE, xlab="Scale",ylab="Sample entropy") #### calcualate the multiscale entropy of peak-to-peak intervals scale_PP <- 1:10 # setting the scale of entropy MSE <- MSE(PP_interval, tau=scale_PP, m=2, r=0.15, I=40000) Seriesplot.fn(MSE$tau ,MSE$SampEn,points=TRUE, xlab="Scale",ylab="Sample entropy") #### Power Spectral Density (PSD) analysis of the peak-to-peak intervals LowPSD(PP_interval, plot=TRUE,min=1/64, max=1/2) #### MultiFractal Detrended Fluctuation Analysis of the peak-to-peak intervals exponents=seq(3, 9, by=1/4) scale=2^exponents #Vector of scales q=-10:10 #q-order of the moment m=2 #An integer of the polynomial order for the detrending Result <- MFDFA(PP_interval, scale, m, q) MFDFAplot.fn(Result,scale,q,model = TRUE) #### fit the MFDFA result with the extended binomial multifractal model Coeff <- fit.model(Result$Hq,q) Coeff #### Caculate the parameter of multifractal model Para<- -log(Coeff)/log(2);Para[3]=Para[1]-Para[2] names(Para)<-c("Hmax","Hmin","i÷H") Para #### plot MFDFA results by individual data("HqData") PP_Hq <- HqData filenames <- row.names(PP_Hq) q=-10:10 # define the range of q-order of the moment ClassNames <- c(substr(filenames[1:19], start = 1, stop = 3), substr(filenames[20:38], start = 1, stop = 5)) Class <- unique(ClassNames) # Obtain group name col_vec <- rep(NA, nrow(PP_Hq) ) pch_vec <- rep(16, nrow(PP_Hq) ) for( i in 1:length(Class) ) { col_vec[ ClassNames == Class[i] ] <- i } Individualplot.fn(q,PP_Hq,Name=Class,col=col_vec,pch=pch_vec, xlab="q",ylab="Hurst exponent") legend("topright", legend=paste0(Class, "(N=", table( ClassNames ), ")"), col=1:4, cex=1, lty=1, pch=16) #### plot the mean and error bar of by MFDFA results group data("HqData") PP_Hq <- HqData filenames <- row.names(PP_Hq) q <- -10:10 # define the range of q-order of the moment ClassNames <- c(substr(filenames[1:19], start = 1, stop = 3), substr(filenames[20:38], start = 1, stop = 5)) Class <- unique(ClassNames) for (i in 1:length(q)){ Data <- GroupComparison.fn(PP_Hq[,i],ClassNames) Result_mean_vec <- Data[,"Mean"] Result_sd_vec <- Data[,"SE"] if( i == 1 ) { Result_mean_mat <- Result_mean_vec Result_sd_mat <- Result_sd_vec } else { Result_mean_mat <- rbind(Result_mean_mat, Result_mean_vec) Result_sd_mat <- rbind(Result_sd_mat, Result_sd_vec) } } #### Hurst exponent of q 0-10 Groupplot.fn (q[1:10],Result_mean_mat[1:10,],Class,errorbar = Result_sd_mat[1:10,], xRange = NA, yRange = NA, col = NA, pch = rep(16,4), Position = "topright", cex.legend = 1, xlab="q",ylab="Hurst exponent",main = "") #### Hurst exponent of q -10-0 Groupplot.fn (q[11:21],Result_mean_mat[11:21,],Class,errorbar = Result_sd_mat[11:21,], xRange = NA, yRange = NA, col = NA, pch = rep(16,4), Position = "topright", cex.legend = 1, xlab="q",ylab="Hurst exponent",main = "")
The respiratory data of a healthy people.
This is an data to be included in my package
Fantasia Database, PhysioNet
"https://doi.org/10.13026/C2RG61"
function to find the peak-to-peak intervals of a respiratory signal.
find.peaks( y, Fs, lowpass = TRUE, freq = 1, MovingAv = FALSE, W = FALSE, filter = TRUE, threshold = 0.2 )find.peaks( y, Fs, lowpass = TRUE, freq = 1, MovingAv = FALSE, W = FALSE, filter = TRUE, threshold = 0.2 )
y |
a numeric vector, with respiratory data for a regularly spaced time series.. |
Fs |
a positive value. sampling frequency of airflow signal. |
lowpass |
logical. Whether to use low-pass filtering to preprocess the airflow signal. |
freq |
an optional values. Cut-off frequency of low-pass filter. The default value is 1. |
MovingAv |
logical.Whether to use Moving Average to preprocess the airflow signal. |
W |
an optional values. the windows of Moving Average. The default value is equal to the sampling frequency Fs. |
filter |
logical.Whether to filter the points of peaks. |
threshold |
an optional value. A threshold is the minimum height difference between the wave crest and wave trough. The default value is 0.2. |
a dataframe for the information of peaks. "PeakIndex" is the position of the peaks and "PeakHeight" is the height of the peaks
Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals.
data("TestData") # load Data from TestData dataset Fs=50 ## sampling frequency is 50Hz Peaks <- find.peaks(Data[,2],Fs,lowpass=TRUE,freq=1,MovingAv=FALSE, W=FALSE,filter=TRUE,threshold=0.05) Peaksdata("TestData") # load Data from TestData dataset Fs=50 ## sampling frequency is 50Hz Peaks <- find.peaks(Data[,2],Fs,lowpass=TRUE,freq=1,MovingAv=FALSE, W=FALSE,filter=TRUE,threshold=0.05) Peaks
function to fit the result of Multifractal detrended fluctuation analysis (MFDFA) with the extended binomial multifractal model. Return the results as a vector which contain the parameters of the model and the goodness of fit
fit.model(Hq, q)fit.model(Hq, q)
Hq |
a nurmeric vector for the generalized Hurst exponent. |
q |
a vector of integers, q-order of the moment. |
a vector for fitting parameters."a" and "b" is the coefficients of the extended binomial multifractal model. "Goodness" is the goodness of fit
Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals.
data("TestData") # load Data from TestData dataset Fs=50 ## sampling frequency is 50Hz Peaks=find.peaks(Data[,2],Fs) PP_interval=diff(Peaks[,1])/Fs exponents=seq(3, 8, by=1/4) scale=2^exponents q=-10:10 m=2 Result <- MFDFA(PP_interval, scale, m, q) Coeff <- fit.model(Result$Hq,q) Coeffdata("TestData") # load Data from TestData dataset Fs=50 ## sampling frequency is 50Hz Peaks=find.peaks(Data[,2],Fs) PP_interval=diff(Peaks[,1])/Fs exponents=seq(3, 8, by=1/4) scale=2^exponents q=-10:10 m=2 Result <- MFDFA(PP_interval, scale, m, q) Coeff <- fit.model(Result$Hq,q) Coeff
function to calculate the statistics for each Group: Number of Samples, mean, standard deviation (SD), standard error (SE), median, confident interval, p-value of ANOVA
GroupComparison.fn(Data, GroupName, na.rm = TRUE, conf.level = 0.95)GroupComparison.fn(Data, GroupName, na.rm = TRUE, conf.level = 0.95)
Data |
vector for response values |
GroupName |
vector for group names |
na.rm |
whether to remove value for calculation |
conf.level |
confidence level |
a dataframe for the statistics for each Group. Number of Samples, mean
standard deviation (SD), median, upper and lower bounds of CI, p-value of ANOVA
Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals.
data("HqData") PP_Hq <- HqData filenames <- row.names(PP_Hq) q <- -10:10 ClassNames <- c(substr(filenames[1:19], start = 1, stop = 3), substr(filenames[20:38], start = 1, stop = 5)) Class <- unique(ClassNames) Data <- GroupComparison.fn(PP_Hq[,1],ClassNames) Datadata("HqData") PP_Hq <- HqData filenames <- row.names(PP_Hq) q <- -10:10 ClassNames <- c(substr(filenames[1:19], start = 1, stop = 3), substr(filenames[20:38], start = 1, stop = 5)) Class <- unique(ClassNames) Data <- GroupComparison.fn(PP_Hq[,1],ClassNames) Data
function to plot the mean and error bar of sample entropy or the MFDFA results by group
Groupplot.fn( x, Average, GroupName, errorbar = NA, xRange = NA, yRange = NA, col = NA, pch = NA, Position = "topright", cex.legend = 0.75, xlab = "", ylab = "", main = "" )Groupplot.fn( x, Average, GroupName, errorbar = NA, xRange = NA, yRange = NA, col = NA, pch = NA, Position = "topright", cex.legend = 0.75, xlab = "", ylab = "", main = "" )
x |
a vector for x axis. |
Average |
Matrix for average in each group |
GroupName |
a vector of names for each group |
errorbar |
matrix for value of erorr bar |
xRange |
range for the x-axis |
yRange |
range for the y-axis |
col |
a vector for the colors to indicate groups |
pch |
a vector for points types to indicate groups |
Position |
position for the legend |
cex.legend |
cex for legend |
xlab |
a title for the x axis |
ylab |
a title for the y axis |
main |
main title for the plot |
No value returned
Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals.
data("HqData") PP_Hq <- HqData filenames <- row.names(PP_Hq) q <- -10:10 ClassNames <- c(substr(filenames[1:19], start = 1, stop = 3), substr(filenames[20:38], start = 1, stop = 5)) Class <- unique(ClassNames) for (i in 1:length(q)){ Data <- GroupComparison.fn(PP_Hq[,i],ClassNames) Result_mean_vec <- Data[,"Mean"] Result_sd_vec <- Data[,"SE"] if( i == 1 ) { Result_mean_mat <- Result_mean_vec Result_sd_mat <- Result_sd_vec } else { Result_mean_mat <- rbind(Result_mean_mat, Result_mean_vec) Result_sd_mat <- rbind(Result_sd_mat, Result_sd_vec) } } Groupplot.fn (q[1:10],Result_mean_mat[1:10,],Class,errorbar = Result_sd_mat[1:10,], xRange = NA, yRange = NA, col = NA, pch = rep(16,4), Position = "topright", cex.legend = 1, xlab="q",ylab="Hurst exponent",main = "") Groupplot.fn (q[11:21],Result_mean_mat[11:21,],Class,errorbar = Result_sd_mat[11:21,], xRange = NA, yRange = NA, col = NA, pch = rep(16,4), Position = "topright", cex.legend = 1, xlab="q",ylab="Hurst exponent",main = "")data("HqData") PP_Hq <- HqData filenames <- row.names(PP_Hq) q <- -10:10 ClassNames <- c(substr(filenames[1:19], start = 1, stop = 3), substr(filenames[20:38], start = 1, stop = 5)) Class <- unique(ClassNames) for (i in 1:length(q)){ Data <- GroupComparison.fn(PP_Hq[,i],ClassNames) Result_mean_vec <- Data[,"Mean"] Result_sd_vec <- Data[,"SE"] if( i == 1 ) { Result_mean_mat <- Result_mean_vec Result_sd_mat <- Result_sd_vec } else { Result_mean_mat <- rbind(Result_mean_mat, Result_mean_vec) Result_sd_mat <- rbind(Result_sd_mat, Result_sd_vec) } } Groupplot.fn (q[1:10],Result_mean_mat[1:10,],Class,errorbar = Result_sd_mat[1:10,], xRange = NA, yRange = NA, col = NA, pch = rep(16,4), Position = "topright", cex.legend = 1, xlab="q",ylab="Hurst exponent",main = "") Groupplot.fn (q[11:21],Result_mean_mat[11:21,],Class,errorbar = Result_sd_mat[11:21,], xRange = NA, yRange = NA, col = NA, pch = rep(16,4), Position = "topright", cex.legend = 1, xlab="q",ylab="Hurst exponent",main = "")
The Hurst exponent extracted from the MFDFA result of respiratory data.
This is an data to be included in my package
Fantasia Database, PhysioNet
"https://doi.org/10.13026/C2RG61"
function to plot multiscale entropy or MFDFA results by individual.
Individualplot.fn( x, y, Name = NA, xRange = NA, yRange = NA, col = NA, pch = NA, Position = "topright", cex.legend = 0.75, xlab = "", ylab = "", main = "" )Individualplot.fn( x, y, Name = NA, xRange = NA, yRange = NA, col = NA, pch = NA, Position = "topright", cex.legend = 0.75, xlab = "", ylab = "", main = "" )
x |
a vector for x-axis coordinate. |
y |
Matrix for response values. |
Name |
vector of names for each line. |
xRange |
range for the x-axis. |
yRange |
range for the y-axis. |
col |
vector for the colors to indicate groups. |
pch |
vector for points types to indicate groups. |
Position |
position for the legend. |
cex.legend |
cex for legend. |
xlab |
a title for x axis. |
ylab |
a title for y axis. |
main |
main title for the picture. |
No value returned
Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals.
data("HqData") PP_Hq <- HqData filenames <- row.names(PP_Hq) q=-10:10 ClassNames <- c(substr(filenames[1:19], start = 1, stop = 3), substr(filenames[20:38], start = 1, stop = 5)) Class <- unique(ClassNames) col_vec <- rep(NA, nrow(PP_Hq) ) pch_vec <- rep(16, nrow(PP_Hq) ) for( i in 1:length(Class) ) { col_vec[ ClassNames == Class[i] ] <- i } Individualplot.fn(q,PP_Hq,Name=Class,col=col_vec,pch=pch_vec, xlab="q",ylab="Hurst exponent") legend("topright", legend=paste0(Class, "(N=", table( ClassNames ), ")"), col=1:4, cex=1, lty=1, pch=16)data("HqData") PP_Hq <- HqData filenames <- row.names(PP_Hq) q=-10:10 ClassNames <- c(substr(filenames[1:19], start = 1, stop = 3), substr(filenames[20:38], start = 1, stop = 5)) Class <- unique(ClassNames) col_vec <- rep(NA, nrow(PP_Hq) ) pch_vec <- rep(16, nrow(PP_Hq) ) for( i in 1:length(Class) ) { col_vec[ ClassNames == Class[i] ] <- i } Individualplot.fn(q,PP_Hq,Name=Class,col=col_vec,pch=pch_vec, xlab="q",ylab="Hurst exponent") legend("topright", legend=paste0(Class, "(N=", table( ClassNames ), ")"), col=1:4, cex=1, lty=1, pch=16)
function to calculate the power spectral density (PSD) of a time series. Methods derived from A. Eke (2000) "Physiological time series: distinguishing fractal noises from motions" <doi:10.1007/s004249900135>.
LowPSD(series, plot = TRUE, min = 1/8, max = 1/2)LowPSD(series, plot = TRUE, min = 1/8, max = 1/2)
series |
a numeric vector, with data for a regularly spaced time series. |
plot |
logical. whether to draw the plot of log power vs. log frequency |
min, max
|
the optional values. Frequency range of power spectral density. The default value is 1/2 and 1/8 and cannot be set to a negative number |
a value of spectral exponent(beta) which the the slope of the of the fitting line on plot of log power vs. log frequency
Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals.
data("TestData") Fs <- 50 Peaks <- find.peaks(Data[,2],Fs,lowpass=TRUE,freq=1,MovingAv=FALSE, W=FALSE,filter=TRUE,threshold=0.05) PP_interval=diff(Peaks[,1])/Fs LowPSD(series=PP_interval,plot=TRUE,min=1/64, max=1/2)data("TestData") Fs <- 50 Peaks <- find.peaks(Data[,2],Fs,lowpass=TRUE,freq=1,MovingAv=FALSE, W=FALSE,filter=TRUE,threshold=0.05) PP_interval=diff(Peaks[,1])/Fs LowPSD(series=PP_interval,plot=TRUE,min=1/64, max=1/2)
Applies the MultiFractal Detrended Fluctuation Analysis (MFDFA) to time series.
MFDFA(tsx, scale, m, q)MFDFA(tsx, scale, m, q)
tsx |
Univariate time series (must be a vector). |
scale |
Vector of scales. |
m |
An integer of the polynomial order for the detrending. |
q |
q-order of the moment. |
A list of the following elements:
Hq q-order Hurst exponent.
tau_q Mass exponent.
hq Holder exponent.
Dq singularity dimension.
Fqi q-order fluctuation function.
line linear fitting line of fluctuation function.
Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals.
data("TestData") # load Data from TestData dataset Fs <- 50 Peaks <- find.peaks(Data[,2],Fs,lowpass=TRUE,freq=1,MovingAv=FALSE, W=FALSE,filter=TRUE,threshold=0.05) head(Peaks) PP_interval <- diff(Peaks$PeakIndex)/Fs ## Computing Multifractal exponents=seq(3, 9, by=1/4) scale=2^exponents q=-10:10 m=2 Result <- MFDFA(PP_interval, scale, m, q) Coeff <- fit.model(Result$Hq,q) print(Coeff) Para<- -log(Coeff)/log(2) Para[3]=Para[1]-Para[2] names(Para)<-c("Hmax","Hmin","DeltaH") Para PP_Hq <- Result$Hq PP_hq <- Result$hq PP_Dq <- Result$Dq PP_Para <-Paradata("TestData") # load Data from TestData dataset Fs <- 50 Peaks <- find.peaks(Data[,2],Fs,lowpass=TRUE,freq=1,MovingAv=FALSE, W=FALSE,filter=TRUE,threshold=0.05) head(Peaks) PP_interval <- diff(Peaks$PeakIndex)/Fs ## Computing Multifractal exponents=seq(3, 9, by=1/4) scale=2^exponents q=-10:10 m=2 Result <- MFDFA(PP_interval, scale, m, q) Coeff <- fit.model(Result$Hq,q) print(Coeff) Para<- -log(Coeff)/log(2) Para[3]=Para[1]-Para[2] names(Para)<-c("Hmax","Hmin","DeltaH") Para PP_Hq <- Result$Hq PP_hq <- Result$hq PP_Dq <- Result$Dq PP_Para <-Para
function to plot the results of MFDFA analysis: q-orde fluctuation function, Hurst exponent,mass exponent and multifractal spectrum. The fitting result of binomial multifratcal model can be also shown by the fitting line
MFDFAplot.fn( Result, scale, q, cex.lab = 1.6, cex.axis = 1.6, col.points = 1, col.line = 1, lty = 1, pch = 16, lwd = 2, model = TRUE, cex.legend = 1 )MFDFAplot.fn( Result, scale, q, cex.lab = 1.6, cex.axis = 1.6, col.points = 1, col.line = 1, lty = 1, pch = 16, lwd = 2, model = TRUE, cex.legend = 1 )
Result |
a list of the MFDFA results. |
scale |
a vector of scales used to calculate the MFDFA results. |
q |
a vector, q-order of the moment used to calculate the MFDFA results. |
cex.lab |
the size of the tick label numbers/text with a numeric value of length 1.The default value is 1.6. |
cex.axis |
the size of the axis label text with a numeric value of length 1.The default value is 1.6. |
col.points |
color of the and point. |
col.line |
color of the line |
lty |
line types. |
pch |
points types. |
lwd |
line width. |
model |
whether to use the model to fit the results and draw a line of fit. |
cex.legend |
the size of the legend text with a numeric value of length 1.The default value is 1. |
No value returned
Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals.
data("TestData") Fs=50 ## sampling frequency is 50Hz Peaks <- find.peaks(Data[,2],Fs,lowpass=TRUE,freq=1,MovingAv=FALSE, W=FALSE,filter=TRUE,threshold=0.05) PP_interval=diff(Peaks[,1])/Fs exponents=seq(3, 9, by=1/4) scale=2^exponents q=-10:10 m=2 Result <- MFDFA(PP_interval, scale, m, q) MFDFAplot.fn(Result,scale,q)data("TestData") Fs=50 ## sampling frequency is 50Hz Peaks <- find.peaks(Data[,2],Fs,lowpass=TRUE,freq=1,MovingAv=FALSE, W=FALSE,filter=TRUE,threshold=0.05) PP_interval=diff(Peaks[,1])/Fs exponents=seq(3, 9, by=1/4) scale=2^exponents q=-10:10 m=2 Result <- MFDFA(PP_interval, scale, m, q) MFDFAplot.fn(Result,scale,q)
function to calculate Moving Average of a series
MovingAverage(y, W)MovingAverage(y, W)
y |
a numeric vector, with respiratory data for a regularly spaced time series. |
W |
a Positive integer, the windows of Moving Average. |
A new numeric vector after calculate the moving average.
Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals.
data("TestData") W <- 50 y <- MovingAverage(Data[,2],W)data("TestData") W <- 50 y <- MovingAverage(Data[,2],W)
function to perform a multiscale entropy (MSE) analysis of a regularly spaced time series. Return the results as an R data frame. Methods derived from Madalena Costa(2002) "Multiscale entropy analysis of complex physiologic time series" <doi:10.1103/PhysRevLett.89.068102>.
MSE(x, tau, m, r, I)MSE(x, tau, m, r, I)
x |
a numeric vector, with data for a regularly spaced time series. NA's are not allowed (because the C program is not set up to handle them). |
tau |
a vector of scale factors to use for MSE. Scale factors are positive integers that specify bin size for the MSE algorithm: the number ofconsecutive observations in 'x' that form a bin and are averaged in the first step of the algorithm. Must be a sequence of equally-spaced integers starting at 1. The largest value must still leave a sufficient number of bins to estimate entropy. |
m |
a positive integers giving the window size for the entropy calculations in the second step of the algorithm, Typical values are 1, 2, or 3. |
r |
a positive value of coefficients for similarity thresholds, such as r=0.15, r*sd(y) must be in the same units as 'x'. Averages in two bins are defined to be similar if they differ by 'r*sd(y)' or less. NOTE: Currently only a single threshold is allowed per run; i.e.,'r' must be a scalar. |
I |
the maximal number of points to be used for calculating MSE |
A data frame with with one row for each combination of 'tau', 'm' and 'rSD'. Columns are "tau", "m", "rSD", and "SampEn" (the calculated sample entropy). The data frame will also have an attribute "SD", the standard deviation of 'x'. rSD = r*sd(y)
Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals.
data("TestData") # load Data from TestData dataset oldoptions <- options(scipen=999) Fs <- 50 # sampling frequency scale_raw <- seq(1,90,by=2) MSER <- MSE(Data[1:10000,2], tau=scale_raw, m=2, r=0.15, I=40000) print(MSER) options(oldoptions)data("TestData") # load Data from TestData dataset oldoptions <- options(scipen=999) Fs <- 50 # sampling frequency scale_raw <- seq(1,90,by=2) MSER <- MSE(Data[1:10000,2], tau=scale_raw, m=2, r=0.15, I=40000) print(MSER) options(oldoptions)
function to plot series data. including Respiration data and Peak-to-Peak intervals series.
Seriesplot.fn( x, y, xRange = NA, yRange = NA, points = TRUE, pch = 1, col.point = 1, cex.point = 1, line = TRUE, lty = 1, col.line = 1, lwd = 1, xlab = "x", ylab = "y", main = "" )Seriesplot.fn( x, y, xRange = NA, yRange = NA, points = TRUE, pch = 1, col.point = 1, cex.point = 1, line = TRUE, lty = 1, col.line = 1, lwd = 1, xlab = "x", ylab = "y", main = "" )
x |
a vector for the x-axis coordinate of a sequence. |
y |
a vector for the y-axis coordinates of a sequence. |
xRange |
range for the x-axis. |
yRange |
range for the y-axis. |
points |
whether to draw the points of the sequence. If points = TRUE, a sequence of points will be plotted. otherwise, will not plot the points. |
pch |
points types. |
col.point |
color code or name of the points. |
cex.point |
cex of points |
line |
whether to draw a line of the sequence. If line = TRUE, a line of sequence will be plotted. otherwise, will not plot the line. |
lty |
line types. |
col.line |
color code or name of the line. |
lwd |
line width. |
xlab |
a title for the x axis. |
ylab |
a title for the y axis. |
main |
main title for the picture. |
No value return
Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals.
data("TestData") oldpar <- par(mfrow=c(1,2)) Seriesplot.fn(Data[1:10000,1],Data[1:10000,2],points=FALSE,xlab="Time(s)",ylab="Respiration") Fs=50 ## sampling frequency is 50Hz Peaks <- find.peaks(Data[,2],Fs,lowpass=TRUE,freq=1,MovingAv=FALSE, W=FALSE,filter=TRUE,threshold=0.05) PP_interval=diff(Peaks[,1])/Fs Seriesplot.fn(1:length(PP_interval),PP_interval,points=FALSE,xlab="Count",ylab="Interval(s)") par(oldpar)data("TestData") oldpar <- par(mfrow=c(1,2)) Seriesplot.fn(Data[1:10000,1],Data[1:10000,2],points=FALSE,xlab="Time(s)",ylab="Respiration") Fs=50 ## sampling frequency is 50Hz Peaks <- find.peaks(Data[,2],Fs,lowpass=TRUE,freq=1,MovingAv=FALSE, W=FALSE,filter=TRUE,threshold=0.05) PP_interval=diff(Peaks[,1])/Fs Seriesplot.fn(1:length(PP_interval),PP_interval,points=FALSE,xlab="Count",ylab="Interval(s)") par(oldpar)