An End-to-End Atrial Fibrillation Detection by A Novel Residual-Based Temporal Attention Convolutional Neural Network with Exponential Nonlinearity Loss

This repository provides source code of RTA-CNN for ECG signal classification and atrial fibrillation detection.

Dataset

To evaluate our method, we used The dataset of The 2017 PhysioNet Challenge. The challenge aims to encourage the development of algorithms to classify, from a single short ECG lead recording (between 30 s and 60 s in length), whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an alternative rhythm, or is too noisy to be classified.

Requirements

• Tensorflow version >=1.13.1.
• Keras version >=2.2.4
• Some basic python packages such as Numpy, Matplotlib, pandas.

Usage

The experiments are all done in 4-fold cross-validation configuration.
The directory hierarchy should be like this:

RTA-CNN/
├── folds
│   ├── fold0
│   │   ├── data
│   │   ├── label
│   │   ├── AF
|   |   |   ├──data
|   |   |   └── label
│   │   ├── normal
│   │   └── other
│   ├── fold012
│   │   ├── data
│   │   └── label
│   ├── fold013
│   ├── fold023
│   ├── fold123
│   ├── fold1
│   ├── fold2
│   └── fold3
└── logs
    ├── ex0
    │   └── models
    ├── ex1
    ├── ex2
    └── ex3

To train on fold 1,2,3 and validate on fold 0:

python main.py --expeiment-index 0