README.md

metadata

title: Arrhythmia Detection using ECG + PPG
emoji: 🩺
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 3.50.2
app_file: app.py
pinned: false

🩺 Arrhythmia Detection using ECG + PPG

This project is a Deep Learning-based arrhythmia detection system that classifies signals into:

• Bradycardia
• Tachycardia
• Ventricular Fibrillation (VFib)
• Ventricular Tachycardia (VTach)
• Normal

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🔍 Input

Upload .csv files containing:

• Time
• ECG (II/III/AVF/I)
• PPG/Pleth

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⚙️ Model

• Architecture: CNN + LSTM
• Input: Raw segmented ECG and PPG signals (20s window, 10s overlap)
• Framework: TensorFlow/Keras

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🧠 Output

• Predicted Class
• Confidence Score
• Segment-wise classification
• Optional signal visualization (first 10 seconds)

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🚀 Created by

Mathivani | Final Year B.Tech Biomedical Engineering
SRM Institute of Science and Technology