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CNN GAP

About

  • This work is related to Physionet 2017 contest. (https://physionet.org/content/challenge-2017/1.0.0/)
  • This work aim at classifying ECG signals into 4-types, including "Atrial Fibrillation", "Normal", "Other Diseases" and "Noisy".
  • We reproduced works of the paper "Towards Understanding ECG Rhythm Classification Using Convolutional Neural Networks and Attention Mappings" by GoodFellow et. al.

Our Works

  • Train on N/A/O 3 type and got F1 score 0.84. (same as the paper)
  • Observe CAM on ECG of each type and find what the model have learned.
  • Train N/A/O/W 4 type with a fine-tuning procedure on the model pretrained by 3 type, and got F1 score 0.81.
  • Fine-tuning the previous model with F1 loss and got F1 score 0.82, which is higher than all of the work.
  • Analyze convergence property of F1 loss and WCE.
  • Analyze convergence property of Softmax and Log-Softmax activation.

Files discription

  • Net/ : All models network structure
  • utils/ : Preprocess function and loss function
  • report/ : All .ppt of this work
  • dataset/ : Dataset
  • jupyter notebook/Plot confusion matrix.ipynb : Plot confusion matrix of a model
  • jupyter notebook/plot.py : Plot accuracy, loss or F1 score of the training process
  • jupyter notebook/analyze f1 loss.ipynb : Analysis of convergence speed of F1, recall and precision.
  • jupyter notebook/Train* : Training models
  • model_description.txt : Description of all models