ECG Multi-class classification tutorial

This repository is created to experience ECG arrhythmia classification. It is a multi-class classification problem that aims to diagnose one of five types of arrhythmias. For convenience, an ipynb version of the tutorial is also provided.

Update:

• 2024.11.12 Upload codes

Requirements

This repo is tested with Ubuntu 22.04, PyTorch 2.0.5, Python3.10, and CUDA12.4

pip install -r requirements.txt    

Dataset Installation

MIT-BIH Database Link

The MIT-BIH Arrhythmia Database is a well-known dataset used for the study of ECG arrhythmias. It contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects.

For experiments, we split the dataset into training, validation, and test sets with a ratio of 6:2:2. We saved the signal data and labels as numpy files. You can use the numpy files in the dataset.

Getting started

1. Install the necessary libraries.
2. Set directory paths for your dataset. (options.py)
3. Run train.py

Evaluation

To evaluate the trained model on the test dataset, use test.py

python test.py

References

[1] Moody, George B., and Roger G. Mark. "The impact of the MIT-BIH arrhythmia database." IEEE engineering in medicine and biology magazine 20.3 (2001): 45-50.

Contact

E-mail: jbcha7@yonsei.ac.kr