SleepPrintNet

SleepPrintNet: A Multivariate Multimodal Neural Network based on Physiological Time-series for Automatic Sleep Staging

The SleepPrintNet is made up of four independent modules: EEG temporal feature extraction module, EEG spectral-spatial feature extraction module, EOG feature extraction module, and EMG feature extraction module. After extracting the corresponding features from each module, we use a feature fusion layer to fuse all the features, and finally obtain the classification results through a classification layer.

These are the source code of SleepPrintNet.

Dataset

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

Requirements

• Python 3.6.5
• CUDA 9.0
• CuDNN 7.5.1
• numpy==1.15.0
• sklearn==0.19.1
• tensorflow_gpu==1.8.0
• Keras==2.2.0
• matplotlib==3.0.3

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

• Command Line Parameters

  • Training
    • --batch_size: Training batch size.
    • --epoch: Number of training epochs.
    • --num_fold: Number of folds.
    • --save_model: Save the best model or not.
  • Directory
    • --model_dir: The directory for saving best models of each fold.
    • --data_dir1: The directory of the EEG, EOG signals.
    • --data_dir2: The directory of the EMG signals.
    • --data_dir3: The directory of the spectral-spatial representation of EEG signals.
    • --result_dir: The directory for saving results.

• Input Data Shape

  • EEG_EOG
    • DataSubID.npy : (numOfSamples, numOfChannels, timeLength) -> (numOfSamples, 6 + 2, 30 * 128 * 3)
      • numOFChannels: 6 channels EEG signals and 2 channels EOG signals.
      • timeLength: 30 (s) * 128 (Hz) * 3 (epochs) = 11520
    • LabelSubID.npy: (numOfSamples, )
  • EMG
    • DataSubID.npy : (numOfSamples, numOfChannels, timeLength) -> (numOfSamples, 3, 11520)
      • numOFChannels: 3 channels EMG signals.
      • timeLength: 30 (s) * 128 (Hz) * 3 (epochs) = 11520
    • LabelSubID.npy: (numOfSamples, )
  • fre_spa
    • DataSubID.npy : (numOfSamples, numOfFreqBands, height, width, 1) -> (numOfSamples, 5, 16, 16, 1)
      • numOfFreqBands: 5 frequency bands
      • height, width: 16 px $\times$ 16 px
    • LabelSubID.npy: (numOfSamples, )

• Training

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

  CUDA_VISIBLE_DEVICES=0 python run.py