This code is part of the paper ''A Large Scale Benchmark to Validate Sleep-Wake Scoring Algorithms''. See the full publication at: https://www.nature.com/articles/s41746-019-0126-9
@article{palotti2019benchmark,
title={Benchmark on a large cohort for sleep-wake classification with machine learning techniques},
author={Palotti, Joao and Mall, Raghvendra and Aupetit, Michael and Rueschman, Michael and Singh, Meghna and Sathyanarayana, Aarti and Taheri, Shahrad and Fernandez-Luque, Luis},
journal={npj Digital Medicine},
volume={2},
number={1},
pages={50},
year={2019},
publisher={Nature Publishing Group}
}
- Download MESA datasets from sleepdata.org (https://sleepdata.org/datasets/mesa)
- Process dataset with notebook -- generate_mesa_dataset_task.ipynb
- In general, all scripts will need to preprocess the data (i.e., get the mean activity value in a win of size X). To save time, we use implemented a preprocessing script (sleep_processdataset.py) which the only parameter is the task number. It outputs a file called hdf_task. Run with:
$ python sleep_processdataset.py <taskid>- Run formulas (automatically reads dataset from hdf_task) and process all formulas:
$ python sleep_formulas.py <taskid>- Run machine learning models:
$ python sleep_ml.py <taskid>- Run LSTM/CNN:
$ python sleep_nn.py <taskid> <seq_len:20,50,100> <kind:LSTM,CNN>- Geting the results. 7.1. After running all scripts, a bunch of intermediary result files was created (e.g., task1_formulas.csv). The script ship.sh collect them all and send them to their expected diretory (e.g., result or summary). 7.2. sleep_summary.py generates the summaries files, which are the input for the ipython/jupyter notebooks (result_analysis.ipynb, sleep_auc_plots.ipynb and sleep_plot345.ipynb). Notebooks provide an easy way to visualize and analyze the results, as well as generate the plots used in the manuscript.