A Multi-View Multi-Scale Neural Network for Multi-Label ECG Classification

This is the code for the paper "A Multi-View Multi-Scale Neural Network for Multi-Label ECG Classification"

Dependency

• python>=3.7
• pytorch>=1.7.0
• torchvision>=0.8.1
• numpy>=1.19.5
• tqdm>=4.62.0
• scipy>=1.5.4
• wfdb>=3.2.0
• scikit-learn>=0.24.2

Usage

Configuration

There is a configuration file "config.py", where one can edit both the training and test options.

Stage 1: Training

After setting the configuration, to start training, simply run

python main_train.py

Since MiniRocket's training strategy is slightly different from the others, to start training in MiniRocket, run

python minirocket_train.py

Stage 2: Knowledge Distillation

The multi-view network trained in the first stage is used to train the single-view network, run

python main_distillation.py

Dataset

PTB-XL dataset can be downloaded from PTB-XL, a large publicly available electrocardiography dataset v1.0.1 (physionet.org).

CPSC2018 dataset can be downloaded from The China Physiological Signal Challenge 2018 (icbeb.org)

HFHC dataset can be downloaded from https://tianchi.aliyun.com/competition/entrance/231754/information

Citation

If you find this idea useful in your research, please consider citing:

@article{
  title={A Multi-View Multi-Scale Neural Network for Multi-Label ECG Classification},
}