ECG classification
Dataset
Requirements
- tensorflow
- numpy
- scipy
- pandas
Also, you can use the commandpip3 install -r requirements.txtto install the dependency packages.
In this project, both python2 and python3 are ok(But we strongly suggest that you use python3).
How to Run
- Put the data set in folder.
- Run
merge_dataset.pyto 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.
- After you train the model, use
test.pyto 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.