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Continual Learning of Physiological Signals

CLOPS is a framework that allows neural networks to continually learn from clinical data streaming in over time.

This repository contains the PyTorch implementation of CLOPS. For details, see A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions. [Nature Communications Paper], [blogpost]

Requirements

The CLOPS code requires the following:

• Python 3.6 or higher
• PyTorch 1.0 or higher

Datasets

Download

The datasets can be downloaded from the following links:

1. PhysioNet 2020
2. Chapman
3. Cardiology

Pre-processing

In order to pre-process the datasets appropriately for CLOPS, please refer to the following repository

Training

To train the model(s) in the paper, run this command:

python run_experiments.py

Evaluation

To evaluate the model(s) in the paper, run this command:

python run_experiments.py

Citing

If you use our code in your research, please consider citing with the following BibTex.

@article{kiyasseh2021clinical,
  title={A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions},
  author={Kiyasseh, Dani and Zhu, Tingting and Clifton, David},
  journal={Nature Communications},
  volume={12},
  number={1},
  pages={1--11},
  year={2021},
  publisher={Nature Publishing Group}
}