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cardiosense-ai
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cardiosense-ai
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# ❤️ CardioSense AI ### Deep Learning-Based ECG Analysis System CardioSense AI is a web-based application that analyzes ECG (Electrocardiogram) signals using a Convolutional Neural Network (CNN). It detects abnormalities, calculates heart rate, visualizes ECG signals, and converts raw ECG data into CSV format. --- ## 🔥 Features * 🧠 AI-based ECG Classification (Normal / Abnormal) * ❤️ Heart Rate (BPM) Calculation using R-Peak Detection * 📈 ECG Waveform Visualization with Peak Marking * 🔄 Raw ECG Data → CSV Converter * 🌐 Interactive Web Interface (Flask) --- ## 🛠️ Tech Stack * Python * TensorFlow / Keras (Deep Learning) * Flask (Backend) * Pandas, NumPy (Data Processing) * SciPy (Signal Processing) * Matplotlib (Visualization) * HTML, CSS, JavaScript (Frontend) * Render (Deployment) * GitHub (Version Control) --- ## 📂 Project Structure ~~~ CardioSense-AI/ │ ├── app.py ├── ecg_cnn_model.h5 ├── requirements.txt ├── render.yaml └── README.md ~~~ --- ## ⚙️ Installation (Run Locally) ```bash git clone https://github.com/Ayushdevo/cardiosense-ai.git cd cardiosense-ai pip install -r requirements.txt python app.py ``` Open in browser: http://127.0.0.1:5000/ --- ## 🚀 Deployment (Render) 1. Push code to GitHub 2. Go to Render → New Web Service 3. Connect repository 4. Set: Build Command: ``` pip install -r requirements.txt ``` Start Command: ``` gunicorn app:app --workers=1 --threads=1 --timeout=120 ``` 5. Deploy --- ## 🧠 How It Works 1. Upload ECG CSV file 2. Preprocessing: * Normalize signal * Extract segment 3. CNN Model: * Predict Normal / Abnormal 4. Signal Processing: * Detect peaks (SciPy) * Compute heart rate 5. Output: * Prediction + Confidence * ECG waveform graph --- ## 📊 Model Details * Model: Convolutional Neural Network (CNN) * Input: ECG signal segment * Output: Binary classification * Dataset: MIT-BIH Arrhythmia Dataset --- ## ⚠️ Limitations * Free hosting may be slow (cold start) * High memory usage due to TensorFlow * Not intended for medical diagnosis --- ## 👨💻 Author **Ayush Tiwari** ECE + AI | IIT Guwahati --- ## 📌 Future Improvements * Real-time ECG monitoring * Mobile app integration * Lightweight model optimization * Multi-class arrhythmia detection --- ## ⭐ Acknowledgements * MIT-BIH ECG Database * TensorFlow & Keras * SciPy Signal Processing --- ## 🚀 Live Demo 👉 https://cardiosense-ai-i.onrender.com
