L-SeqSleepNet

This is source code for L-SeqSleepNet described in the paper below. We used the SleepEDF-20 dataset to demonstrate how the package works. Please note that the implementation is not optimized in any sense.

• Huy Phan, Kristian P Lorenzen, Elisabeth Heremans, Oliver Y Chén, Minh C Tran, Philipp Koch, Alfred Mertins, Mathias Baumert, Kaare Mikkelsen, Maarten De Vos. L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging. IEEE Journal of Biomedical and Health Informatics (JBHI), vol. 27, no. 10, pp., 2023. [PDF] [Preprint]

This repo contains:

• L-SeqSleepNet implementation in Tensorflow 1
• The prepared SleepEDF-20 database in mat files
  • (Note: You can refer to other my other repos for how to process the data, for example https://github.com/pquochuy/xsleepnet)
• Leave-one-subject-out experimental setup
• L-SeqSleepNet model weights pretrained on SHHS data

Environment:

• Python3.7
• Tensorflow GPU 1.x (x >= 13) (for network training and evaluation)
• numpy
• scipy
• h5py
• sklearn
• imblearn

How to use:

1. Clone this repo which contains the prepared SleepEDF-20 database in mat files
2. Training/finetuning and testing
   • cd ./edf/network/lseqsleepnet/
   • bash run_training_repeat1.sh to train the network from scratch. The bash script includes the commands to do leave-one-subjet-out cross-validation training and testing.
   • bash run_finetune_repeat1.sh to finetune from the SHHS-preptrained model. The bash script includes the commands to do leave-one-subjet-out cross-validation finetuning and testing.
3. Evaluation
   • bash evaluate_performance.sh to evaluate the performance of the traning/finetuning experiments done in Step 2.

License

CC-BY-NC-4.0