--- a/README.md
+++ b/README.md
@@ -1,92 +1,85 @@
-# SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
-In this study, we introduced a novel deep learning approach, called SleepEEGNet, for automated sleep stage scoring using a single-channel EEG.
-
-# Paper
- Our paper can be downloaded from the [arxiv website](https://arxiv.org/pdf/1903.02108).
- * The Model architecture
-  ![Alt text](/images/seq2seq_sleep.jpg)  
-  
- * The CNN architecture  
- 
-  ![Alt text](/images/seq2seq_cnn.jpg)
- 
-## Requirements
-* Python 2.7
-* tensorflow/tensorflow-gpu
-* numpy
-* scipy
-* matplotlib
-* scikit-learn
-* matplotlib
-* imbalanced-learn(0.4.3)
-* pandas
-* mne
-## Dataset and Data Preparation
-We evaluated our model using [the Physionet Sleep-EDF datasets](https://physionet.org/physiobank/database/sleep-edfx/) published in 2013 and 2018.  
-We have used the source code provided by [github:akaraspt](https://github.com/akaraspt/deepsleepnet) to prepare the dataset.
-
-* To download SC subjects from the Sleep_EDF (2013) dataset, use the below script:
-
-```
-cd data_2013
-chmod +x download_physionet.sh
-./download_physionet.sh
-```
-
-* To download SC subjects from the Sleep_EDF (2018) dataset, use the below script:
-```
-cd data_2018
-chmod +x download_physionet.sh
-./download_physionet.sh
-```
-
-Use below scripts to extract sleep stages from the specific EEG channels of the Sleep_EDF (2013) dataset:
-
-```
-python prepare_physionet.py --data_dir data_2013 --output_dir data_2013/eeg_fpz_cz --select_ch 'EEG Fpz-Cz'
-python prepare_physionet.py --data_dir data_2013 --output_dir data_2013/eeg_pz_oz --select_ch 'EEG Pz-Oz'
-```
-
-## Train
-
-* Modify args settings in seq2seq_sleep_sleep-EDF.py for each dataset.
-
-* For example, run the below script to train SleepEEGNET model with the 20-fold cross-validation using Fpz-Cz channel of the Sleep_EDF (2013) dataset:
-```
-python seq2seq_sleep_sleep-EDF.py --data_dir data_2013/eeg_fpz_cz --output_dir output_2013 --num_folds 20
-```
-
-## Results
-* Run the below script to present the achieved results by SleepEEGNet model for Fpz-Cz channel.
-```
-python summary.py --data_dir output_2013/eeg_fpz_cz
-```
-
-![Alt text](/images/results.jpg)
-
-## Visualization
-* Run the below script to visualize attention maps of a sequence input (EEG epochs) for Fpz-Cz channel.
-```
-python visualize.py --data_dir output_2013/eeg_fpz_cz
-```
-
-
-## Citation
-
-If you find it useful, please cite our paper as follows:
-
-```
-@article{mousavi2019sleepEEGnet,
-  title={SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach},
-  author={Sajad Mousavi, Fatemeh Afghah and U. Rajendra Acharya},
-  journal={arXiv preprint arXiv:1903.02108},
-  year={2019}
-}
-```
-
-## References
- [github:akaraspt](https://github.com/akaraspt/deepsleepnet)  
- [deepschool.io](https://github.com/sachinruk/deepschool.io/blob/master/DL-Keras_Tensorflow)
- 
-## Licence 
-For academtic and non-commercial usage 
+# SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
+In this study, we introduced a novel deep learning approach, called SleepEEGNet, for automated sleep stage scoring using a single-channel EEG.
+
+# Paper
+ Our paper can be downloaded from the [arxiv website](https://arxiv.org/pdf/1903.02108).
+
+ 
+## Requirements
+* Python 2.7
+* tensorflow/tensorflow-gpu
+* numpy
+* scipy
+* matplotlib
+* scikit-learn
+* matplotlib
+* imbalanced-learn(0.4.3)
+* pandas
+* mne
+## Dataset and Data Preparation
+We evaluated our model using [the Physionet Sleep-EDF datasets](https://physionet.org/physiobank/database/sleep-edfx/) published in 2013 and 2018.  
+We have used the source code provided by [github:akaraspt](https://github.com/akaraspt/deepsleepnet) to prepare the dataset.
+
+* To download SC subjects from the Sleep_EDF (2013) dataset, use the below script:
+
+```
+cd data_2013
+chmod +x download_physionet.sh
+./download_physionet.sh
+```
+
+* To download SC subjects from the Sleep_EDF (2018) dataset, use the below script:
+```
+cd data_2018
+chmod +x download_physionet.sh
+./download_physionet.sh
+```
+
+Use below scripts to extract sleep stages from the specific EEG channels of the Sleep_EDF (2013) dataset:
+
+```
+python prepare_physionet.py --data_dir data_2013 --output_dir data_2013/eeg_fpz_cz --select_ch 'EEG Fpz-Cz'
+python prepare_physionet.py --data_dir data_2013 --output_dir data_2013/eeg_pz_oz --select_ch 'EEG Pz-Oz'
+```
+
+## Train
+
+* Modify args settings in seq2seq_sleep_sleep-EDF.py for each dataset.
+
+* For example, run the below script to train SleepEEGNET model with the 20-fold cross-validation using Fpz-Cz channel of the Sleep_EDF (2013) dataset:
+```
+python seq2seq_sleep_sleep-EDF.py --data_dir data_2013/eeg_fpz_cz --output_dir output_2013 --num_folds 20
+```
+
+## Results
+* Run the below script to present the achieved results by SleepEEGNet model for Fpz-Cz channel.
+```
+python summary.py --data_dir output_2013/eeg_fpz_cz
+```
+
+## Visualization
+* Run the below script to visualize attention maps of a sequence input (EEG epochs) for Fpz-Cz channel.
+```
+python visualize.py --data_dir output_2013/eeg_fpz_cz
+```
+
+
+## Citation
+
+If you find it useful, please cite our paper as follows:
+
+```
+@article{mousavi2019sleepEEGnet,
+  title={SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach},
+  author={Sajad Mousavi, Fatemeh Afghah and U. Rajendra Acharya},
+  journal={arXiv preprint arXiv:1903.02108},
+  year={2019}
+}
+```
+
+## References
+ [github:akaraspt](https://github.com/akaraspt/deepsleepnet)  
+ [deepschool.io](https://github.com/sachinruk/deepschool.io/blob/master/DL-Keras_Tensorflow)
+ 
+## Licence 
+For academtic and non-commercial usage