https://github.com/cardiokit/vecg
Representational Learning of Single-Lead Electrocardiogram Signals using Beta-TCVAE
Science Score: 36.0%
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Low similarity (15.0%) to scientific vocabulary
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Representational Learning of Single-Lead Electrocardiogram Signals using Beta-TCVAE
Basic Info
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- Stars: 1
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- Forks: 0
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Metadata Files
README.md
Disentangled Representational Learning of Single Lead Electrocardiogram Signals using Variational Autoencoder
This work focuses on clustering 1-lead electrocardiogram (ECG) heartbeats using beta total correlation variational autoencoder ($\beta$-TCVAE). The objective is to detect irregular morphologies in ECG signals, which can serve as indicators of cardiac anomalies.
Installation and Setup
To get started with the project, follow these steps:
- Make sure you have Python version 3.10 installed.
- Create a virtual environment
- Install the required libraries by running the following command in the project directory. Some requirements might need adjustemnt depending on your hardware and OS:
conda create -n ecg python=3.10 conda activate ecg pip install -r requirements.txt
Data Preparation
The raw ECG data is available in a remote repository and needs to be downloaded and built. Therefore, perform the following steps:
- Clone the ECG-TFDS repository:
git clone https://github.com/CardioKit/ECG-TFDS - Install the requirements for ECG-TFDS:
pip install -r ./ECG-TFDS/requirements.txt - Change to the ECG-TFDS source directory (e.g., Zheng's dataset):
cd ./ECG-TFDS/src/zheng - Build the dataset:
tfds build --register_checksums
Running the Code
Execute the main file to run the code:
python main.py
The main file requires a configuration file for parameterization:
options:
-h, --help show this help message and exit
-p, --path_config location of the params file (default: ./params.yml)
Evaluation
The results of the runs can be analyzed with the jupyter notebook:
./analysis/article.ipynb
How to cite?
If you want to either use code or refer to results, please cite the following article: (To be determined)
@article{kapsecker2025disentangled,
title={Disentangled representational learning for anomaly detection in single-lead electrocardiogram signals using variational autoencoder},
author={Kapsecker, Maximilian and Möller, Matthias C and Jonas, Stephan M},
journal={Computers in Biology and Medicine},
volume={184},
pages = {109422},
year = {2025},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2024.109422},
url = {https://www.sciencedirect.com/science/article/pii/S0010482524015075},
}
Owner
- Name: CardioKit
- Login: CardioKit
- Kind: organization
- Email: max.kapsecker@tum.de
- Location: Germany
- Website: www.cardiokit.org
- Repositories: 1
- Profile: https://github.com/CardioKit
A system composed of tools for the analysis of electrocardiogram signals.
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