# 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

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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.
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### For Single Lead ECG


### A. ECG preprocessing and segmentation

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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.
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### 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

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