RespirAnalyzer (version 1.0.2)

RespirAnalyzer-package: tools:::Rd_package_title("RespirAnalyzer")

Description

tools:::Rd_package_description("RespirAnalyzer")

Author

tools:::Rd_package_author("RespirAnalyzer")

Maintainer: tools:::Rd_package_maintainer("RespirAnalyzer")

Details

The R package RespirAnalyzer contains functions for analyzing respiratory data.

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 = "")