Deform-CNN:使用pytorch的可变形卷积网络用于心电图诊断

# Deform-CNN Deformable convolutional networks for electrocardiogram diagnosis using pytorch. This is the source code of the article "***An End-to-End 12-Leading Electrocardiogram Diagnosis System based on Deformable Convolutional Neural Network with Good Anti-noise Ability***" # Getting started ### Training environment + NVIDIA driver version 418.67 + CUDA Version 10.1 + Python version 3.6.8 + Pytorch version 1.4.0+cu101 ### Install required package From the root of this repository, run ```pip install -r requirements.txt``` ### Download the dataset We use CPSC-2018 as dataset. You can dowload from [here](http://2018.icbeb.org/Challenge.html) Reprocessing the data and save the data in `.npy` format. Then place the data into folder `./dataset/DataSet250HzRepeatFill` and place the label into folder `./dataset`. For more detail, please refer to `load_dataset.py`. *Notice:In our experiment, we removed data with total length more than 30 seconds, reducing the sampling frequency to 250 Hz and repeating filling. For more detail please refer to our article* ### Start training From the root of this repository, run ```python dcnv2_ecg_train.py``` # Customization If you need to adjust some parameters, you can use argument. For example: ```python dcnv2_ecg_train.py --epochs 20 --optimizer Adam``` For more arguments, please refer to `dcnv2_ecg_train.py`. # Additional Notes Please contact us by [creating an issue](https://github.com/HeartbeatAI/Deform-CNN/issues/new/choose) if you would like to use this project for commercial purposes. Reference: https://github.com/4uiiurz1/pytorch-deform-conv-v2 (MIT License)