# tools:::Rd_package_title("RespirAnalyzer") tools:::Rd_package_description("RespirAnalyzer") package ## Details The R package RespirAnalyzer contains functions for analyzing respiratory data. ## Author(s) tools:::Rd_package_author("RespirAnalyzer") Maintainer: tools:::Rd_package_maintainer("RespirAnalyzer") ## References Zhang T, Dong X, Chen C, Wang D, Zhang XD. RespirAnalyzer: an R package for continuous monitoring of respiratory signals. ## Examples ```r # 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-package