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---
license: afl-3.0
language:
- en
tags:
- applewatch
- ecg
- electrocardiogram
- hyperkalemia
- esrd
- deeplearning
---
# 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 / Apple Watch 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
``` |