Our paper can be downloaded from the arxiv website
We evaluated our model using the PhysioNet MIT-BIH Arrhythmia database
Modify args settings in seq_seq_annot_aami.py for the intra-patient ECG heartbeat classification
Modify args settings in seq_seq_annot_DS1DS2.py for the inter-patient ECG heartbeat classification
Run each file to reproduce the model described in the paper, use:
python seq_seq_annot_aami.py --data_dir data/s2s_mitbih_aami --epochs 500
python seq_seq_annot_DS1DS2.py --data_dir data/s2s_mitbih_aami_DS1DS2 --epochs 500
If you find it useful, please cite our paper as follows:
@article{mousavi2018inter,
title={Inter-and intra-patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach},
author={Mousavi, Sajad and Afghah, Fatemeh},
journal={arXiv preprint arXiv:1812.07421},
year={2018}
}
For academtic and non-commercial usage