This project performs basic ECG signal analysis using the MIT-BIH Arrhythmia Database.
It focuses on extracting key cardiac features such as R-peaks, heart rate, and RR-interval variability.

The goal is to create a beginner-friendly but complete biomedical signal analysis workflow suitable for academic learning, internships, and introductory research projects.

• Load ECG data from the MIT-BIH Arrhythmia Database
• Plot raw ECG waveform
• Detect R-peaks using signal processing
• Calculate:
  • Total beats
  • Average heart rate (bpm)
  • Bradycardia / Tachycardia classification
• Save results as:
  • PNG plots
• Well-documented Google Colab notebook for easy execution

1. Open the notebooks/ecg_analysis.ipynb file in Google Colab.
2. Download the MIT-BIH Arrhythmia Database from: https://physionet.org/content/mitdb/1.0.0/
3. Upload the extracted folder to your Colab session.
4. Update the path in the notebook if needed.
5. Run the notebook cells to generate:
   • ECG plots
   • R-peak detection visualization
   • Heart rate and RR-interval statistics

Install the required Python packages:

pip install wfdb scipy matplotlib numpy pandas

All generated outputs are stored in the results/ folder:

• ecg_plot.png
• r_peaks_plot.png
• ecg_analysis_results.csv

These files are produced automatically when the notebook runs.

• Add Butterworth filtering for noise removal
• Classify arrhythmias using MIT-BIH annotations
• Build a simple GUI to upload ECG images
• Compare multiple ECG records automatically

This project uses publicly available data from PhysioNet under their open-data license.