--- a/README.md
+++ b/README.md
@@ -1,54 +1,52 @@
-# 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 
-
+# 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
+ 
+## 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
+```
+
+## 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 
+