rsleep: A R package for sleep data analysis
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 :
remotes::install_github("boupetch/rsleep@dev")Stable version can be downloaded and installed from CRAN:
install.packages("rsleep")Usage
library(rsleep)Vignettes
- Spectral analysis of sleep electroencephalography signals
- Spindles detection and analysis
- 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
Detecting spindles
Computing a transition matrix
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. 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. 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.
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
Santhiya P., JebaRajalakshmi J., S Siva Ranjani, Selvi S. ArunMozhi, Detection of Epilepsy through Machine Learning Algorithms Using Brain Signals, 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. 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. 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. 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. 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.