/SEN-DAL

Multi-modal Physiological Signals based Squeeze-and-Excitation Network with Domain Adversarial Learning for Sleep Staging

Primary LanguagePython

SEN-DAL

Multi-modal Physiological Signals based Squeeze-and-Excitation Network with Domain Adversarial Learning for Sleep Staging

model

The multi-modal physiological signals based Squeeze-and-Excitation Network with Domain Adversarial Learning (SEN-DAL) is a deep learning model for sleep staging. The SEN-DAL is made up of two independent feature extraction networks for modeling the heterogeneity, a Multi-modal Squeeze-and-Excitation feature fusion module for adaptively utilizing the multi-modal signals, and a Domain Adversarial Learning module to extract subject-invariant sleep feature.

This repository contains the source code of the SEN-DAL.

Dataset

We evaluate our model on the Montreal Archive of Sleep Studies (MASS) dataset Subset-3. The MASS dataset is an open-access and collaborative database of laboratory-based polysomnography (PSG) recordings.

Requirements

  • Python 3.6.2
  • CUDA 9.0
  • CuDNN 7.6.4
  • numpy==1.19.5
  • sklearn==0.19.1
  • tensorflow_gpu==1.8.0
  • Keras==2.2.0

Usage

  • Preprocess

    • Prepare data (raw signals) in data_dir
      • Files name: 01-03-00XX-Data.npy, where XX denotes subject ID.
      • Tensor shape: [sample, channel=26, length], where channel 0 is ECG channel, 1-20 are EEG channels, 21-23 are EMG channels, 24-25 are EOG channels.
    • Modify directory
      • Modify data_dir and label_dir in run_preprocess.py accordingly.
      • (Optional) Modify output_dir in run_preprocess.py.
    • Run preprocess program
      • cd preprocess
      • python run_preprocess.py

  • Training Command Line Arguments

    • Training
      • --batch_size: Training batch size.
      • --epoch: Number of training epochs.
      • --num_fold: Number of folds.
      • --save_model: Save the best model or not.
      • --early_stop: Use early stop trick or not.
      • --gpunums: Number of GPU(s).
      • --from_fold: Training start fold.
      • --to_fold: Training end fold.
    • Directory
      • --label_dir: The directory of labels.
      • --data_dir_eog: The directory of the EOG signals.
      • --data_dir_psd: The directory of the spectral-spatial representation of EEG signals.
      • --output: The directory for saving results.
      • --model_dir: The directory for saving best models of each fold.

  • Training

    Run run.py with the command line arguments. By default, the model can be run with the following command:

    CUDA_VISIBLE_DEVICES=0 python run.py