Exploring Best Practices for ECG Signal Processing in Machine Learning

What this project does:

In this project we:

1. Download and process all physionet ECG files for quick dataloading.
2. Deploy pytorch models on these datasets (courtesy of TSAI:https://github.com/timeseriesAI/tsai)
   • Any pytorch model can be used here, dataloaders can also be modified.
3. Create custom signal processing functions to transform datasets before they are given to the model.
   • You add your own in transformation_funcs.py

Requirements

This project makes heavy use of the tsai library (version 0.3.2), which requires pytorch. We recommend use of the conda environment. You can clone our conda environment with conda create --name <env> - conda activate - install the requirements with pip install -r pip-requirements.txt

How to get started:

1. run this shell script to download physionet files: get_data.sh
2. run parse_data.ipynb notebook to convert the datasets to numpy arrays (this helps with very quick dataloading)
3. run inception.ipynb to see how to make a dataloaders and run one of TSAI's models on the data (you can use any pytorch compatible model):
4. run transformation.ipynb to play around with transformations
5. run experiment_analysis_*.ipynb to see how we analyzed experiment results