MetaSleepLearner (https://ieeexplore.ieee.org/document/9258375)
N. Banluesombatkul et al., "MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2020.3037693.
Five publicly datasets were used to evaluate our method including
- MASS (http://massdb.herokuapp.com/en/) - Permission required
- SleepEDF (https://physionet.org/content/sleep-edf/1.0.0/)
- ISRUC (https://sleeptight.isr.uc.pt)
- UCD (https://physionet.org/content/ucddb/1.0.0/)
- CAP (https://physionet.org/content/capslpdb/1.0.0/)
[All of them should be prepared and put in /data - only MASS datasets were pre-processed using bandpass filters as described in our paper]
- set up configuration i.e. data path, model hyperparameters, etc. in
configure.py - meta-train (our approach)
python MAML.py - normal pre-train (baseline)
python NormalPretrain.py - fine-tune: configure and run
python FinetuneCNNKFolds.py - evaluate: put list of fine-tune weights path and run notebook
FinetuneAndTestOnBestHyperparams-List.ipynb
- Every file: set GPU# before running
- bot.py: add your chat ID and bot token (if you want to have notification, otherwise just remove all lines calling it.)
To cite our paper,
@ARTICLE{9258375,
author={N. {Banluesombatkul} and P. {Ouppaphan} and P. {Leelaarporn} and P. {Lakhan} and B. {Chaitusaney} and N. {Jaimchariya} and E. {Chuangsuwanich} and W. {Chen} and H. {Phan} and N. {Dilokthanakul} and T. {Wilaiprasitporn}},
journal={IEEE Journal of Biomedical and Health Informatics},
title={MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning},
year={2020},
volume={},
number={},
pages={1-1},
doi={10.1109/JBHI.2020.3037693}}