MousaviSajad / ECG-Heartbeat-Classification-seq2seq-model

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

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

Requirements

Dataset

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

Train

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

Alt text

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