Pulse2PressureNet is a deep learning–based biomedical AI system that estimates
Systolic and Diastolic Blood Pressure from Photoplethysmography (PPG) signals using a
CNN–LSTM hybrid architecture with real-time deployment through Streamlit.
🔗 App: https://pulse2pressurenet-uanltapgwn29ft2q883tpj.streamlit.app/ 🔗 Repository: https://github.com/Skywalker-organa/Pulse2PressureNet
- ✔️ Cuffless & non-invasive BP estimation from PPG signals
- ✔️ CNN + LSTM hybrid deep learning architecture
- ✔️ Real-time inference via Streamlit web application
- ✔️ Supports synthetic + real physiological signal datasets
- ✔️ Interactive visualization dashboards & analytics
- CNN Layers → Learn waveform morphology
- LSTM Layers → Capture temporal cardiovascular dynamics
- Dense Layers → Predict Systolic & Diastolic BP
- Framework: Python · PyTorch
- Python
- PyTorch
- NumPy
- Pandas
- Streamlit
- Matplotlib
This application is built for research and educational purposes only and is not a medical device.
It should not be used for clinical decision making.
Sanjana Shyamsundar
Biomedical AI • Deep Learning • Healthcare Technology