This package is a collection of tools used at the Adelaide Institute for Sleep Health for the analysis of polysomnography files.

WARNING: PSGA is still a work in progress, use at your own risk. Consider using YASA for a more stable alternative.

The tools were developed in Python 3.7 and require the following dependencies: MNE, pandas and scikit-learn.

PSGA comes with a user-friendly dashboard to quickly visualise your data. The dashboard additionaly requires Pyside2 and plotly. The dashboard is not designed for manual scoring of events. If you are after an UI to score sleep, consider using visbrain-sleep.

If you find this code useful, please consider citing the following publication:

Lechat, B., Hansen, K. L., Melaku, Y. A., Vakulin, A., Micic, G., Adams, R. J., . . . Zajamsek, B. (2021). A Novel EEG Derived Measure of Disrupted Delta Wave Activity during Sleep Predicts All-Cause Mortality Risk. Ann Am Thorac Soc, (in press). doi:10.1513/AnnalsATS.202103-315OC

Several functions were adapted or inspired from MNE-features and YASA. Credit should be given to functions/modules adapted from these packages. If credit is missing, please let us know.