| # Kardio-Net: A Deep Learning Model for Predicting Serum Potassium Levels from Apple Watch ECG in ESRD Patients | |
| ### I. Input preparation | |
| 1. Prerequisites | |
| Ensure that all ECG files are saved in *.npy format with the following shapes: | |
| - (5000, 12) for 12-lead ECGs | |
| - (500 * 30 seconds, 1) for Apple Watch ECGs | |
| Store these files in a single flat directory, and include a manifest CSV file in the same location to accompany | |
| them. | |
| 2. Manifest File Format | |
| The manifest CSV file should include a header. | |
| Each row corresponds to one ECG file with the following columns: | |
| - filename: Name of the .npy file (without the extension). | |
| - label: serum potassium label. | |
| ### II. Inference | |
| ### For 12-Lead ECG | |
| <!-- #region --> | |
| 1. use predict_potassium_12lead.py to get the potassium level prediction. | |
| 2. Edit data_path and manifest_path and run the predict_potassium.py script. | |
| 3. Upon completion, a file named "dataloader_0_predictions.csv" will be saved in the same directory. This file contains the inference results "preds" from the model. | |
| 4. Use generate_result.py to get the performance metric and figure. | |
| <!-- #endregion --> | |
| ### For Single Lead ECG | |
| ### A. ECG preprocessing and segmentation | |
| <!-- #region --> | |
| 1. Use preprocessing.py for denoise, normalize, and segment ECG into 5-second for input | |
| 2. Set the following paths in preprocessing.py: | |
| - raw_directory = "path/to/raw_data_directory" #raw ecg folder for target task | |
| - output_directory = "path/to/output_directory" #output folder for normalize ECG | |
| - manifest_path = "/path/to/manifest.csv" # Manifest file path | |
| - output_path = "path/to/output_path" # Output path for segmented ECGs | |
| 3. Execute predict.py. | |
| 4. If the ECG files were already normalize, can execute the segmentation function only. | |
| <!-- #endregion --> | |
| ### B. potassium regression model | |
| <!-- #region --> | |
| 1. use predict_potassium_1lead.py to get the potassium level prediction. | |
| 2. Edit data_path and manifest_path and run the predict_potassium.py script. | |
| 3. Upon completion, a file named "dataloader_0_predictions.csv" will be saved in the same directory. This file contains the inference results "preds" from the model. | |
| 4. Use generate_result.py to get the performance metric and figure. | |
| <!-- #endregion --> | |
| ```python | |
| ``` | |