rfonod / deepsleep2 Goto Github PK

A 300-second example of a 13-channel physiological recording and the corresponding sleep arousal prediction/target labels.

DeepSleep 2.0 is a compact version of DeepSleep, a state-of-the-art, U-Net-inspired, fully convolutional deep neural network, which achieved the highest unofficial score in the 2018 PhysioNet Computing Challenge. The proposed network architecture has a compact encoder/decoder structure containing only 740,551 trainable parameters. The input to the network is a full-length multi-channel polysomnographic recording signal. The network has been designed and optimized to efficiently predict non-apnea sleep arousals on held-out test data at a 5-millisecond resolution level, while not compromising the prediction accuracy. When compared to DeepSleep, the obtained experimental results in terms of gross area under the precision-recall curve (AUPRC) and gross area under the receiver operating characteristic curve (AUROC) suggest that a lightweight architecture, which can achieve similar prediction performance at a lower computational cost, is realizable.

It is assumed that you have the full or partial PhysioNet dataset (~135 GB of data per folder) on the disk. In ./data, you can find two bash scripts to download the PhysioNet dataset.

Here are the essential steps to sucesfully run the main Jupyter notebook file (deep_sleep2.ipynb).

STEP 0: Clone the Repository

 git clone https://github.com/rfonod/deepsleep2.git
 cd deepsleep2

STEP 1: Installation

1. Install Python and PyTorch. Python 3.8 and PyTorch 1.8.1 were considered for the reported results in the DeepSleep 2.0 paper
2. [OPTIONAL] Create a virtual environment with a specific version of Python
3. Install Python dependencies listed in requirements.txt. You can run:

pip3 install -r requirements.txt

4. If you plan to use GPU computations (recommended), install CUDA

STEP 2: Hyperparameters

A correctly set up hyperparameters.txt file must be present in a subdirectory of ./models. The subdirectory name is specified in the MODEL_NAME variable.

STEP 3: Notebook File

Run the cells of deep_sleep2.ipynb in a sequential order. Consider the description of the Main Switches section.

If you use this code in your research, please cite the following publication:

@Article{Fon22a,
  author    = {Fonod, Robert},
  title     = {{DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning}},
  journal   = {AI},
  year      = {2022},
  volume    = {3},
  number    = {1},
  pages     = {164-179},
  doi       = {https://doi.org/10.3390/ai3010010},
  publisher = {MDPI},
}

Consider also citing the original DeepSleep paper.