https://github.com/boupetch/rsleep
Sleep Data Analysis with R
https://github.com/boupetch/rsleep
cran electroencephalography heart-rate-variability mdf r r-package signals sleep sleep-data-analysis sleep-record
Last synced: 3 months ago
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Sleep Data Analysis with R
- Host: GitHub
- URL: https://github.com/boupetch/rsleep
- Owner: boupetch
- License: other
- Created: 2019-07-10T14:16:26.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2024-06-02T07:07:53.000Z (almost 2 years ago)
- Last Synced: 2025-10-22T04:55:22.129Z (7 months ago)
- Topics: cran, electroencephalography, heart-rate-variability, mdf, r, r-package, signals, sleep, sleep-data-analysis, sleep-record
- Language: R
- Homepage:
- Size: 2.74 MB
- Stars: 15
- Watchers: 2
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
rsleep: A R package for sleep data analysis
================
[](https://cran.r-project.org/package=rsleep)
[](https://cran.r-project.org/package=rsleep)
[](https://doi.org/10.5281/zenodo.10507974)
[](https://github.com/sponsors/boupetch)
rsleep: Open-source, multiplatform R package for advanced sleep data analysis. Features automatic sleep scoring and sophisticated visualization tools.
## Installation
Development version can be directly installed from
[Github](https://github.com/boupetch/rsleep) :
``` r
remotes::install_github("boupetch/rsleep@dev")
```
Stable version can be downloaded and installed from
[CRAN](https://cran.r-project.org/):
``` r
install.packages("rsleep")
```
## Usage
``` r
library(rsleep)
```
## Vignettes
- [Spectral analysis of sleep electroencephalography signals](https://rsleep.org/articles/Spectral_analysis_sleep_electroencephalography.html)
- [Spindles detection and analysis](https://rsleep.org/articles/Spindles_detection_and_analysis.html)
- [Using Rsleep and SleepCycles Packages to Detect Sleep Cycles](https://rsleep.org/articles/Using_rsleep_and_SleepCycles_packages_to_detect_sleep_cycles.html)
## Examples
### Plotting a spectrogram
[
](https://rsleep.org/articles/Spectral_analysis_sleep_electroencephalography.html)
### Detecting R peaks in ECG signal
[
](https://rsleep.org/reference/detect_rpeaks.html)
### Processing a hypnogram
[
](https://rsleep.org/reference/hypnogram.html)
### Plotting a hypnodensity
[
](https://rsleep.org/reference/plot_hypnodensity.html)
### Detecting spindles
[
](https://rsleep.org/reference/a7.html)
### Computing a transition matrix
[
](https://rsleep.org/reference/transitions.html)
# Citation
```
@software{paul_bouchequet_2024_10507974,
author = {Paul Bouchequet},
title = {rsleep},
doi = {10.5281/zenodo.7416363},
url = {https://doi.org/10.5281/zenodo.7416363}
}
```
## Publications using the rsleep package
- Lok, R., Duran, M., & Zeitzer, J. M. (2023). [Moving time zones in a flash with light therapy during sleep.](https://www.nature.com/articles/s41598-023-41742-w) In Scientific Reports (Vol. 13, Issue 1). Springer Science and Business Media LLC.
- Baur, D. M., Dornbierer, D. A., & Landolt, H.-P. [Concentration-effect relationships of plasma caffeine on EEG delta power and cardiac autonomic activity during human sleep.](https://www.medrxiv.org/content/10.1101/2023.10.14.23297036v1) Cold Spring Harbor Laboratory.
- 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.](https://arinex.com.au/EMBC/pdf/full-paper_271.pdf)
- P. Bouchequet, T. Andrillon, G. Solelhac, A. Rouen, F. Sauvet, and D. Léger, [0424 Visualizing insomnia phenotypes using dimensionality reduction techniques,](https://academic.oup.com/sleep/article/46/Supplement_1/A188/7181658) SLEEP, vol. 46, no. Supplement_1. Oxford University Press (OUP), pp. A188–A189, May 01, 2023
- Santhiya P., JebaRajalakshmi J., S Siva Ranjani, Selvi S. ArunMozhi, [Detection of Epilepsy through Machine Learning Algorithms Using Brain Signals](https://www.proquest.com/openview/5244c8f8e90715c223df74f2487651dc/1?pq-origsite=gscholar&cbl=2035897), NeuroQuantology, Bornova Izmir Vol. 20, Iss. 8, (2022): 6011 - 6018.
- Rajalakshmi, J., Ranjani, S. S., Sugitha, G., & Prabanand, S. C. (2022). [Electroencephalogram Data Analysed Through the Lens of Machine Learning to Detect Signs of Epilepsy.](https://ieeexplore.ieee.org/document/9985641) In 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA). 2022 IEEE.
- Altınkaya Z, Öztürk L, Büyükgüdük İ, et al. [Non-invasive vagus nerve stimulation in a hungry state decreases heart rate variability.](https://www.sciencedirect.com/science/article/abs/pii/S0031938422003213) Physiology & Behavior. 2023;258:114016.
- Munch Nielsen, J., Zibrandtsen, I. C., Masulli, P., Lykke Sørensen, T., Andersen, T. S., & Wesenberg Kjær, T. (2022). [Towards a wearable multi-modal seizure detection system in epilepsy: A pilot study.](https://www.sciencedirect.com/science/article/pii/S1388245722000219?via%3Dihub) In Clinical Neurophysiology (Vol. 136, pp. 40–48). Elsevier BV. https://doi.org/10.1016/j.clinph.2022.01.005
- Rajalakshmi J, Ranjani SS, Sugitha G, Prabanand SC. [Electroencephalogram Data Analysed Through the Lens of Machine Learning to Detect Signs of Epilepsy.](https://ieeexplore.ieee.org/document/9985641) 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.](https://www.sciencedirect.com/science/article/pii/S1389945722005378) Sleep Medicine. 2022;100:S129.
- Chang K-M, Liu P-T, Wei T-S. [Electromyography Parameter Variations with Electrocardiography Noise.](https://www.mdpi.com/1424-8220/22/16/5948) 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.](https://osf.io/preprints/psyarxiv/9s43u/) 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.](https://www.jmir.org/2021/10/e26476/) J Med Internet Res. 2021;23:e26476.
- Arts F. [Predicting Subjective Team Performance Using Multimodal, Single-Modality and Segmented Physiological Data](https://arno.uvt.nl/show.cgi?fid=156733) Thesis, 2020.
- Andrillon T, Solelhac G, Bouchequet P, et al. [Revisiting the value of polysomnographic data in insomnia: more than meets the eye.](https://www.sciencedirect.com/science/article/abs/pii/S1389945719316442) Sleep Medicine. 2020;66:184-200.