Web app to diagnose types of Arrhythmia from user-uploaded ECG signals using Machine Learning and Deeep Learning models.
The data used to train and test the models used by this app comes from the PhysioNet 2017 AF Classification Challenge.
Try the app yourself here.
- Andres Ruiz Calvo
- Daniel De Las Cuevas Turel
- Enrique Botía Barbera
- Simon E. Sanchez Viloria
- Zijun He
- python>=3.6
# Create a virtual environment and install dependencies
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
# start the streamlit app
streamlit run app/main.pyAfter running the commands above you'll be able to access the app from your local browser.
Use Docker to start the streamlit server to demo the app.
docker build -t ecg-classification .
# Wait until the image is built...
docker run ecg-classification-
Clone this repository.
-
Go to the notebooks folder and explore our analysis or open the notebook used to train the model here
-
Train the models yourself or load the pre-trained weights that we've provided in the models/ folder.
-
Run the streamlit app to see the results.
Goldberger, A., et al. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220." (2000).
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details.
| You can also store here documentation made with any other package.
│ e.g: pdoc --html ../src, make html, ...
|
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── app <- Directory where the web app is runned.
│ ├── requirements.txt <- specific dependencies of the streamlit app
│ |
│ ├── prod-config.toml <- configuration file for production
│ |
| └───streamlit_ecg <- Files used by the streamlit app.
│ ├── ecg.py <- The streamlit app itself.
│ ├── validation/ <- sample ECGs for demonstration.
│ └── model.mod <- The model used by the app to make predictions.
|
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
|
└── src <- Source code for use in this project.
├── __init__.py <- Makes src a Python module
│
├── data <- Scripts to download or generate data
│ └── make_dataset.py
│
├── features <- Scripts to turn raw data into features for modeling
│ └── build_features.py
│
├── models <- Scripts to train models and then use trained models to make
│ │ predictions
│ ├── predict_model.py
│ └── train_model.py
│
└── visualization <- Scripts to create exploratory and results oriented visualizations
└── visualize.py
Project based on the cookiecutter data science project template. #cookiecutterdatascience