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https://github.com/colinwu0403/ecg_afib
ECG (Electrocardiography) Signal Analysis and (AFIB) Atrial Fibrillation Detection
https://github.com/colinwu0403/ecg_afib
Last synced: 6 days ago
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ECG (Electrocardiography) Signal Analysis and (AFIB) Atrial Fibrillation Detection
- Host: GitHub
- URL: https://github.com/colinwu0403/ecg_afib
- Owner: ColinWu0403
- Created: 2024-05-23T08:46:40.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-05-23T09:15:48.000Z (8 months ago)
- Last Synced: 2024-05-23T09:53:57.265Z (8 months ago)
- Language: Python
- Size: 479 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# ECG Signal Analysis and Atrial Fibrillation (AFib) Detection Models
[](https://www.python.org/downloads/release/python-3117/)
## About
This project contains a collection of classification models in Python that predict if a patient has Atrial Fibrillation (AFib) based on the given Electrocardiography (ECG) signals of the patient.
The models are written in Python using `scikit-learn` and `TensorFlow Keras`. `wfdb` and `Neurokit2` are used to analyze ECG signals and measure data to export them to a dataset (in the [data](data) folder as .csv files).
The ECG signals are taken from the open-source MIT-BIH Atrial Fibrillation Database (in [data/afdb/](data/afdb) and the PTB-XL ECG Database (in [data/ptb/](data/ptb)).
The [afdb](data/afdb) and [ptb](data/ptb) folders contain the respective database signals, but the actual files are not pushed as they are too large. You can download them on the official website (the link is in the citation below).
The [models](models) folder contains the generated files for the models, also not pushed to GitHub.
The [reports](reports) folder contains the auto-generated report for each model, including the accuracy and confusion matrix.
## Install required libraries
```
install --no-cache-dir -r requirements.txt
```
## Models
### Random Forest Classifier
Used RandomForestClassifier from `sci-kit learn` to classify an ECG signal on a 10-second interval as Normal or AFIB.
### LSTM
Used `TensorFlow` to create a 3-layer LSTM (RNN) model to classify ECG signals on 10-second intervals as Normal or AFIB.
### CNN
Used `TensorFlow` to create a 3-layer CNN model to classify ECG signals on 10-second intervals as Normal or AFIB.
### SVM
Used SVC from `sci-kit learn` to classify ECG signals on 10-second time intervals as Normal or AFIB.
### Gradient Boost
Used `xgboost` to create a Gradient Boosting model to classify ECG signals on 10-second intervals as Normal or AFIB.
### Resnet
Used `TensorFlow` to create a Resnet model with 1 Convolutional layer and 2 Residual blocks to classify ECG signals on 10-second intervals as Normal or AFIB.
## References
- Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. https://doi.org/10.13026/C2MW2D
- Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Lespinasse, F., Pham, H., Schölzel, C., & Chen, S. A. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods, 53(4), 1689–1696. https://doi.org/10.3758/s13428-020-01516-y
- Wagner, P., Strodthoff, N., Bousseljot, R., Samek, W., & Schaeffter, T. (2022). PTB-XL, a large publicly available electrocardiography dataset (version 1.0.3). PhysioNet. https://doi.org/10.13026/kfzx-aw45.
- Xie, C., McCullum, L., Johnson, A., Pollard, T., Gow, B., & Moody, B. (2023). Waveform Database Software Package (WFDB) for Python (version 4.1.0). PhysioNet. https://doi.org/10.13026/9njx-6322.