mehulcse / ecg-anomaly

ECG Anomaly Detection using convolutional neural network

Classification

The repository contains code for Master's degree dissertation - ECG Anomaly Detection using convolutional neural network.

The repository follows config principle and can be run in the following modes:

• Training - use train.py --config configs/training/ECGNet.json to train the model
• Validation - use inference.py --config configs/inference/config.json to validate the model

All available models and all necessary information are described below

Python 3.7 and PyTorch are used in the project

Getting started

Training quick start:

1. Download and unzip files into mit-bih directory
2. Install requirements via pip install -r requirements.txt
3. Generate 2D data files running cd scripts && python dataset-generation-pool.py
4. Create json annotation files
   • For 2D model - cd scripts && python annotation-generation-2d.py
5. Run training - python train.py --config configs/training/ECGNet.json