yangjunlei12 / PhysioNet

Deep learning based ECG classification

ECG classification

Dataset

physionet challenge 2017

Requirements

• tensorflow
• numpy
• scipy
• pandas
  Also, you can use the command pip3 install -r requirements.txt to install the dependency packages.
  In this project, both python2 and python3 are ok(But we strongly suggest that you use python3).

How to Run

1. Put the data set in folder.
2. Run merge_dataset.py to create train.mat and test.mat. Use the following command to run the code.

python3 merge_dataset.py --dir YOUR_TRAINING_SET_FOLDER_NAME

Use python3 merge_dataset.py -h if you need some help.
3. Run train.py. You can choose your parameter for the following parameters in your command.

• learning_rate
• epochs
• batch_size.
• k_folder: True/False.

If you want to begin the process for k-folder validation, use the following command: python3 train.py --k_folder True. If you only want to train the model, use the command: python3 train.py. Use python3 train.py -h if you need some help.

4. After you train the model, use test.py to test the accuracy and F1 rate. The default path for checkpoints is checkpoints/. If you use other path, run the test.py use the following command:

python3 test.py --check_point_folder YOUR_CHECKPOINT_FOLDER_PATH

Experiment result

The F1 for our model is 0.82. But maybe if you run you will get a different number for that the training and testing set is randomly choose.