A real-time, portable ECG monitoring solution powered by Edge AI. This system filters ECG signals, detects anomalies, and provides visualization through a 7" touchscreen interface using a Raspberry Pi and a custom DSP-based board.
Video Demo : https://drive.google.com/drive/folders/1yti6jyNpjtF1IYLHnfJoG0vB7w5mRzdy?usp=drive_link
This project enables intelligent ECG monitoring using Edge AI technologies. The system:
- Captures ECG signals via the AD8232 module and electrodes
- Filters the signal in real-time
- Detects QRS complexes and rhythm anomalies
- Displays live signal and health insights on a 7" touchscreen via the Raspberry Pi
- 🕒 Real-Time Signal Processing
- 🧹 Noise Filtering using IIR Filters
- 📈 QRS Complex & Anomaly Detection
- 🧠 Edge AI Inference (Local Model)
- 📊 Visualization Interface (PyQt5/Matplotlib)
- 🔌 SPI Communication (C on Raspberry Pi)
- 💾 Offline Data Storage
- OS: Raspberry Pi OS 64-bit
- Libraries:
numpy==1.26.4 scipy==1.13.0 matplotlib==3.8.4 pyqt5==5.15.10 spidev==3.6 joblib==1.4.2
Display: 7" HD touchscreen (Luckfox v1.1)
DSP-Based Board Developed by Shanon Technologies
Performs real-time filtering using optimized embedded C
🔌 Hardware Architecture
[AD8232 ECG Sensor] --analog--> [DSP Board] --SPI--> [Raspberry Pi 5] --HDMI--> [7" Touchscreen]
🛠️ Installation Clone the repository
git clone https://github.com/07SAIF07/Edge-AI-Powered-ECG-Monitoring-System.git cd Edge-AI-Powered-ECG-Monitoring-System Install Python dependencies
pip install -r requirements.txt Compile C SPI driver
cd spi_comm/ make
Run the visualization app
python3 main.py
📷 Screenshots
🧪 Model Details
Type: CNN
Trained on: MIT-BIH dataset
Function: Detects arrhythmias and QRS intervals
👨💻 Team
-Saifeddine Brahmi -Yassine Bouzaiene -Taher Bouhlel
Supervised by Mme Chiraz Zribi ENSI Tunisia – Class of 2025
📄 License This project is licensed under the MIT License.