rsleep: A R package for sleep data analysis

rsleep is a multiplatform open-source R package providing a toolbox for sleep data processing, visualization and analysis. rsleep provides tools for state of the art automatic sleep stages scoring.

Installation

Development version can be directly installed from Github using the devtools package :

devtools::install_github("boupetch/rsleep")

Stable version can be downloaded and installed from CRAN:

install.packages("rsleep")

Usage

library(rsleep)

Vignettes

• Spectral analysis of sleep electroencephalography signals
• Using Rsleep and SleepCycles Packages to Detect Sleep Cycles

Examples

Plotting a spectrogram

Detecting R peaks in ECG signal

Processing a hypnogram

Plotting a hypnodensity

Computing a transition matrix

Citation

@software{paul_bouchequet_2022_7474289,
  author       = {Paul Bouchequet},
  title        = {rsleep},
  month        = dec,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {1.0.6},
  doi          = {10.5281/zenodo.7416363},
  url          = {https://doi.org/10.5281/zenodo.7416363}
}

rsleep usage in scientific litterature

• Wolf, M.C., Klein, P., Kulau, U., Richter, C. and Wolf, K.H., DR. BEAT: First Insights into a Study to Collect Baseline BCG Data with a Sensor-Based Wearable Prototype in Heart-Healthy Adults.

• P. Bouchequet, T. Andrillon, G. Solelhac, A. Rouen, F. Sauvet, and D. Léger, 0424 Visualizing insomnia phenotypes using dimensionality reduction techniques, SLEEP, vol. 46, no. Supplement_1. Oxford University Press (OUP), pp. A188–A189, May 01, 2023. doi: 10.1093/sleep/zsad077.0424.

• Altınkaya Z, Öztürk L, Büyükgüdük İ, et al. Non-invasive vagus nerve stimulation in a hungry state decreases heart rate variability. Physiology & Behavior. 2023;258:114016.

• Rajalakshmi J, Ranjani SS, Sugitha G, Prabanand SC. Electroencephalogram Data Analysed Through the Lens of Machine Learning to Detect Signs of Epilepsy. 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA). September 2022.

• Andrillon T, Solelhac G, Bouchequet P, et al. Leveraging machine learning to identify the neural correlates of insomnia with and without sleep state misperception. Sleep Medicine. 2022;100:S129.

• Chang K-M, Liu P-T, Wei T-S. Electromyography Parameter Variations with Electrocardiography Noise. Sensors. 2022;22:5948.

• Kragness HE, Eitel MJ, Anantharajan F, Gaudette-Leblanc A, Berezowska B, Cirelli L. An itsy bitsy audience: Live performance facilitates infants’ attention and heart rate synchronization. psyarxiv.com/9s43u 10.31234/osf.io/9s43u 2022.

• Stucky B, Clark I, Azza Y, et al. Validation of Fitbit Charge 2 Sleep and Heart Rate Estimates Against Polysomnographic Measures in Shift Workers: Naturalistic Study. J Med Internet Res. 2021;23:e26476.

• Arts F. Predicting Subjective Team Performance Using Multimodal, Single-Modality and Segmented Physiological Data Thesis, 2020.

• Andrillon T, Solelhac G, Bouchequet P, et al. Revisiting the value of polysomnographic data in insomnia: more than meets the eye. Sleep Medicine. 2020;66:184-200.