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--- |
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license: afl-3.0 |
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language: |
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- en |
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tags: |
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- applewatch |
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- ecg |
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- electrocardiogram |
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- hyperkalemia |
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- esrd |
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- deeplearning |
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--- |
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# Kardio-Net: A Deep Learning Model for Predicting Serum Potassium Levels from Apple Watch ECG in ESRD Patients |
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### I. Input preparation |
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1. Prerequisites |
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Ensure that all ECG files are saved in *.npy format with the following shapes: |
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- (5000, 12) for 12-lead ECGs |
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- (500 * 30 seconds, 1) for Apple Watch ECGs |
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Store these files in a single flat directory, and include a manifest CSV file in the same location to accompany |
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them. |
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2. Manifest File Format |
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The manifest CSV file should include a header. |
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Each row corresponds to one ECG file with the following columns: |
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- filename: Name of the .npy file (without the extension). |
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- label: serum potassium label. |
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### II. Inference |
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### For 12-Lead ECG |
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<!-- #region --> |
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1. use predict_potassium_12lead.py to get the potassium level prediction. |
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2. Edit data_path and manifest_path and run the predict_potassium.py script. |
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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. |
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4. Use generate_result.py to get the performance metric and figure. |
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<!-- #endregion --> |
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### For Single Lead / Apple Watch ECG |
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### A. ECG preprocessing and segmentation |
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<!-- #region --> |
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1. Use preprocessing.py for denoise, normalize, and segment ECG into 5-second for input |
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2. Set the following paths in preprocessing.py: |
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- raw_directory = "path/to/raw_data_directory" #raw ecg folder for target task |
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- output_directory = "path/to/output_directory" #output folder for normalize ECG |
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- manifest_path = "/path/to/manifest.csv" # Manifest file path |
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- output_path = "path/to/output_path" # Output path for segmented ECGs |
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3. Execute predict.py. |
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4. If the ECG files were already normalize, can execute the segmentation function only. |
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<!-- #endregion --> |
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### B. potassium regression model |
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<!-- #region --> |
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1. use predict_potassium_1lead.py to get the potassium level prediction. |
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2. Edit data_path and manifest_path and run the predict_potassium.py script. |
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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. |
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4. Use generate_result.py to get the performance metric and figure. |
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<!-- #endregion --> |
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<!-- #region --> |
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Paper Link: https://www.sciencedirect.com/science/article/pii/S2405500X24007527 |
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Citation: I-Min Chiu, Po-Jung Wu, Huan Zhang, J. Weston Hughes, Albert J. Rogers, Laleh Jalilian, Marco Perez, Chun-Hung Richard Lin, Chien-Te Lee, James Zou, David Ouyang, Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms, JACC: Clinical Electrophysiology, 2024, ISSN 2405-500X, https://doi.org/10.1016/j.jacep.2024.07.023. |
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<!-- #endregion --> |
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```python |
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``` |
