README

rsleep development version can be directly installed from Github using the devtools package.

Getting sleep data

An example sleep record can be downloaded using the following code line. It contains full polysomnography data recorded over a whole night. Signals form sensors are stored in the European Data Format [2] (EDF) file, while events are stored in the Comma-Separated Values (CSV) file,


download.file("https://osf.io/57j2u/download", "15012016HD.edf", method="curl")

download.file("https://osf.io/h4ysj/download", "15012016HD.csv", method="curl")

Records manipulation

In rsleep, write_mdf() and read_mdf() functions are used to write and read records on disk. Files are converted from the EDF to Morpheo Data Format [3] (MDF). MDF is a simple, efficient and interoperable hierarchical file format for biological timeseries. The format supports raw signal and metadata storage. MDF uses binary files for signals and JSON for metadata.


if(!dir.exists("15012016HD")){
  events <- read_events_noxturnal("15012016HD.csv")

  write_mdf(edfPath = "15012016HD.edf",
            mdfPath = "15012016HD",
            channels = c("C3-M2", "ECG"),
            events = events)
}


events <- read_events_noxturnal("15012016HD.csv")

write_mdf(edfPath = "15012016HD.edf",
          mdfPath = "15012016HD",
          channels = c("C3-M2", "ECG"),
          events = events)

Once written on disk, MDF records can be read using the read_mdf() function. It quickly returns signals, events and metadata as a list.

Sleep Stages

Epoching


reference <- hypnogram(mdf$events)
reference <- reference[-nrow(reference),]

epochs <- epochs(signals = lapply(mdf$channels,function(x){x$signal}),
                 sRates = lapply(mdf$channels,function(x){x$metadata$sRate}),
                 resample = 200,
                 epoch = reference,
                 startTime = as.numeric(as.POSIXct(mdf$metadata$startTime)))

Electroencephalography

Fourier transforms are computed over EEG during sleep since 1942 [5] . Spectrograms of whole night signals can be plotted using the spectrogram function.


spectrogram(signal = mdf$channels$`C3-M2`$signal,
            sRate = mdf$channels$`C3-M2`$metadata$sRate,
            startTime = as.POSIXct(mdf$metadata$startTime))

Spectral powers


bands <- lapply(epochs,function(x){
  apply(x, 2, function(y){
    bands_power(bands = list(c(0.5,3.5),c(3.5,7.5),c(7.5,13),c(13,30)),
                signal = y, sRate = 200,
                broadband = c(0.5,30))
  })
})


c3m2 <- lapply(bands,function(x){
  unlist(x$`C3-M2`)
})
bands_df <- data.frame(matrix(unlist(c3m2), nrow=length(c3m2), byrow=TRUE))

colnames(bands_df) <- c("Delta","Theta","Alpha","Beta")
bands_df$stage <- reference$event
bands_df <- reshape2::melt(bands_df, id="stage")

summary(bands_df)
#>  stage       variable        value         
#>  N3 :1024   Delta:1469   Min.   :0.004375  
#>  N2 :2164   Theta:1469   1st Qu.:0.006235  
#>  N1 :  36   Alpha:1469   Median :0.008367  
#>  REM:1904   Beta :1469   Mean   :0.008254  
#>  AWA: 748                3rd Qu.:0.009860  
#>                          Max.   :0.014397


library(ggplot2)

pal <- c("#FF0000","#00A08A","#F98400","#5BBCD6")
ggplot(bands_df,aes(x=stage,y=value,fill=variable)) + 
  geom_boxplot() + theme_bw() +
  scale_fill_manual(values = pal) +
  theme(legend.title = element_blank()) +
  xlab("") + ylab("Normalized power")

Electrocardiography

detect_rpeaks implements the first part of the Pan & Tompkins algorithm [6] to detect R peaks from an electrocardiogram (ECG) signal.


library(ggplot2)

sRate <- 200

ecg <- data.frame(Volts = example_ecg_200hz,
                  Seconds = c(1:length(example_ecg_200hz))/sRate)

rpeaks <- detect_rpeaks(example_ecg_200hz, sRate)

ggplot(ecg,
       aes(x = Seconds,
           y = Volts)) +
  geom_line() + theme_bw() +
  geom_vline(data.frame(p = rpeaks),
             mapping = aes(xintercept = p),
             linetype="dashed",color = "red")

Statistics computing

Stages & scoring


stages_stats(example_hypnogram_30s)
#> rem_duration  n1_duration  n2_duration  n3_duration awa_duration 
#> 2.380000e+02 4.500000e+00 2.705000e+02 1.280000e+02 9.400000e+01 
#>          tts      rem_tts       n1_tts       n2_tts       n3_tts 
#> 6.410000e+02 3.712949e-01 7.020281e-03 4.219969e-01 1.996880e-01 
#>      awa_tts          tsp   efficiency      latency   n1_latency 
#> 1.466459e-01 7.360000e+02 8.709239e-01 2.200000e+01 0.000000e+00 
#>   n2_latency   n3_latency  rem_latency         waso 
#> 3.300000e+01 5.100000e+01 1.160000e+02 7.300000e+01

References

[1] K. Hornik, The comprehensive r archive network, Wiley Interdisciplinary Reviews: Computational Statistics. 4 (2012) 394–398. https://cran.r-project.org/.

[2] B. Kemp, A. Värri, A.C. Rosa, K.D. Nielsen, J. Gade, A simple format for exchange of digitized polygraphic recordings, Electroencephalography and Clinical Neurophysiology. 82 (1992) 391–393. doi:10.1016/0013-4694(92)90009-7.

[3] P. Bouchequet, D. Jin, G. Solelhac, M. Chennaoui, D. Leger, Morpheo Data Format (MDF), un nouveau format de données simple, robuste et performant pour stocker et analyser les enregistrements de sommeil, Médecine Du Sommeil. 15 (2018) 48–49. doi:10.1016/j.msom.2018.01.130.

[4] AASM Scoring Manual - American Academy of Sleep Medicine, American Academy of Sleep Medicine Association for Sleep Clinicians and Researchers. (n.d.). https://aasm.org/clinical-resources/scoring-manual/.

[5] J.R. Knott, F.A. Gibbs, C.E. Henry, Fourier transforms of the electroencephalogram during sleep., Journal of Experimental Psychology. 31 (1942) 465–477. doi:10.1037/h0058545.

[6] J. Pan, W.J. Tompkins, A Real-Time QRS Detection Algorithm, IEEE Transactions on Biomedical Engineering. BME-32 (1985) 230–236. doi:10.1109/TBME.1985.325532.

rsleep: A R package for sleep data analysis

Installation