xzwbzhang / ecg-heartbeat-classification-seq2seq-model

Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach

Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach

Paper

Our paper can be downloaded from the arxiv website

• The Network architecture

Recruitments

• Python 2.7
• tensorflow/tensorflow-gpu
• numpy
• scipy
• scikit-learn
• matplotlib
• imbalanced-learn (0.4.3)

Dataset

We evaluated our model using the PhysioNet MIT-BIH Arrhythmia database

• To download our pre-processed datasets use this link, then put them into the "data" folder.
• Or you can follow the instructions of the readme file in the "data preprocessing_Matlab" folder to download the MIT-BIH database and perform data pre-processing. Then, put the pre-processed datasets into the "data" folder.

Train

• 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

Results

Citation

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}
}

References

deepschool.io

Licence

For academtic and non-commercial usage