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+# multimodule-ecg-classification
+
+RESEARCH-PAPER:{
+
+      TITLE: "Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification",
+
+      CITE:  "https://ieeexplore.ieee.org/abstract/document/9508527",
+      
+      YEAR: 2021,
+      
+      CONFERENCE: "IEEE EMBS",
+      
+      AUTHORS: ["Duc Le^",
+                    "Vidhiwar Singh Rathour^",
+                          "Sang Truong", 
+                                   "Quan Mai^, 
+                                            Patel Brijesh; 
+                                                    Ngan Le"],
+                                                          ^: Equal Contribution}  
+
+                                                          
+                                                                                    
+DIRECTORY-TREE:{
+
+     data: "Directory: Datasets for training are stored here.",
+    
+     utils: "Directory: Utility based files",
+     
+     examples: {"Directory: github/awni/ecg/":[
+            "Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network"]},
+
+     models:{ "Directory: DNN Models": {
+            resnet_cnn.py: "Python: Pytorch implememntation of Multimodal-ecg-classification, CNN, Word2Vec",
+            resnet_lstm_phy2017.py: "Python: Pytorch implememntation of Multimodal-ecg-classification, LSTM, CNN",
+            resnet_lstm.py: "Python: Pytorch implememntation of Multimodal-ecg-classification, LSTM, CNN",
+            resnet_w2v.py: "Python: Pytorch implememntation of Multimodal-ecg-classification, LSTM, CNN, Word2Vec",
+            resnet_lstm_mitbih.py: "Python: Pytorch implememntation of Multimodal-ecg-classification, LSTM, CNN, Word2Vec, Attention AYN"}},
+
+     ecg_cnn.py: "Python: Pytorch implememntation of Multimodal-ecg-classification, CNN, Resnet",
+     ecg_w2v.py: "Python: Pytorch implememntation of Multimodal-ecg-classification, Word2Vec, Resnet",
+     ecg_lstm.py: "Python: Pytorch implememntation of Multimodal-ecg-classification, LSTM, CNN, LSTM",
+     ecg_phy2017.py:"Python: Pytorch implememntation of Multimodal-ecg-classification, LSTM, CNN",
+     ecg_mitbih.py: "Python: Pytorch implememntation of Multimodal-ecg-classification, LSTM, CNN, Word2Vec, Attention AYN",
+     transform_data.ipynb: "Jupyter Notebook: Python implementation for Data Generation, and Preprocessing"}
+                                       
+HOW-TO-USE:{
+
+      Uno: "Make sure the required libraries (Torch, Panda, Tqdm, ... etc.,). are installed",
+      Dos: : "Use the examples directory to download and preprocess data.",
+      Tres: "Follow transform_data.ipyn to get data ready for training.",
+      Cuatro: "Run python ecg_###.py to train on training data, and validate on validation data",
+      Cinco: "By default results are saved in checkpoints directory"}
+                               
+IMAGES:{ 
+
+ [![Model.png](https://i.postimg.cc/PJw2b5gd/Model.png)](https://postimg.cc/vxGrbb8K)
+ [![Results.png](https://i.postimg.cc/PxmCmGSB/Results.png)](https://postimg.cc/34xrTq5B)}
+
+             
+#  EOF
+                     
+