ECG Data Visualization

This is a basic interactive tool to visualize and analyze ECG (electrocardiogram) data using Streamlit and Plotly. The app allows users to upload ECG data in .mat file format, apply filters, visualize the time-domain and frequency-domain representations of the ECG signal, and view basic signal statistics.

Features

• Upload ECG data in .mat format.
• Apply various signal filters (lowpass, highpass, bandpass).
• Visualize ECG signal in the time domain.
• Visualize the frequency spectrum of the ECG signal (using DFT).
• Display signal statistics (mean, standard deviation, min, max, range).
• Customize plot appearance (line color, width, grid visibility).

Requirements

• Python 3.7 or higher
• Streamlit
• Plotly
• NumPy
• SciPy

Installation

1. Clone the repository

git clone https://github.com/ubadaht/ECGvisualizer.git
cd EcgVisualizer

2. Install dependencies

It’s recommended to use a virtual environment for your project. You can set up a virtual environment and install the dependencies as follows:

python -m venv env
source env/bin/activate  # For Windows, use `env\Scripts\activate`
pip install -r requirements.txt

Alternatively, you can install the necessary packages manually:

pip install streamlit plotly numpy scipy

3. Run the app

After the dependencies are installed, you can run the app with the following command:

streamlit run app.py

This will start the Streamlit server, and you can access the app in your web browser at http://localhost:8501.

Usage

1. Upload ECG Data: Click on the file uploader in the sidebar and select a .mat file containing ECG data.
2. Signal Processing: Choose the filter type (lowpass, highpass, or bandpass) and set the cutoff frequency to process the ECG signal.
3. Visualization: The app will display:
   • A time-domain plot of the ECG signal.
   • A frequency-domain plot (DFT) of the ECG signal.
4. Plot Customization: Customize the plot appearance (line color, width, grid visibility) through the sidebar options.
5. Signal Statistics: View basic statistics such as mean, standard deviation, min, max, and range of the ECG signal.

Contributing

Contributions are welcome! If you find a bug or have suggestions for improvements, please create an issue or submit a pull request.

Acknowledgements

• Streamlit for building interactive web apps with Python.
• Plotly for interactive data visualizations.
• NumPy and SciPy for numerical computing and signal processing.