iobt-vistec / metasleeplearner Goto Github PK

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}}