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+# 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](https://arxiv.org/pdf/1812.07421v2)
+ * The Network architecture
+  ![Alt text](/images/seq2seq_b.jpg)
+ 
+## Requirements
+* 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](https://www.physionet.org/physiobank/database/mitdb/)
+* To download our pre-processed datasets use [this link](https://drive.google.com/drive/folders/19bDrAqlSGQuNLRmA-7pQRU9R81gSuY70?usp=sharing), 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
+  ![Alt text](/images/results.jpg)
+## 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](https://github.com/sachinruk/deepschool.io/blob/master/DL-Keras_Tensorflow)
+ 
+## Licence 
+For academtic and non-commercial usage 
+