RespirAnalyzer-package: Analysis Functions of Respiratory Data

In RespirAnalyzer: Analysis Functions of Respiratory Data

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.

Details

The R package RespirAnalyzer contains functions for analyzing respiratory data.

Author(s)

Xiaohua Douglas Zhang [aut, cph], Teng Zhang [aut], Xinzheng Dong [aut, cre]

Maintainer: Xinzheng Dong <[e-mail]>

References

Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals.

Examples

# load Data from TestData dataset
data("TestData")
Seriesplot.fn(Data[1:2000,1],Data[1:2000,2],points=FALSE,
              xlab="Time(s)",ylab="Respiratory")
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)
points(Data[Peaks[2:13,1],1],Data[Peaks[2:13,1],2],col=2)
PP_interval <- diff(Peaks[,1])/Fs
Seriesplot.fn(1:length(PP_interval),PP_interval,points=FALSE,xlab="Count",
          ylab="Inter-breath Interval(s)")
#### Moving Average
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
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)
#### 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$SampEn,points=TRUE,
              xlab="Scale",ylab="Sample entropy")
#### entropy of IBI
scale_PP <- 1:10
MSE <-  MSE(PP_interval, tau=scale_PP, m=2, r=0.15, I=40000)
Seriesplot.fn(MSE$tau ,MSE$SampEn,points=TRUE,
              xlab="Scale",ylab="Sample entropy")

#### PSD analysis
LowPSD(PP_interval, plot=TRUE,min=1/64, max=1/2)
#### MFDFA
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,model = TRUE)
#### fit.model
Coeff <- fit.model(Result$Hq,q)
Coeff
Para<- -log(Coeff)/log(2);Para[3]=Para[1]-Para[2]
names(Para)<-c("Hmax","Hmin","i÷H")
Para

#### Individualplot
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)

#### Groupplot
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 = "")

RespirAnalyzer documentation built on July 9, 2023, 7 p.m.