A comprehensive Python toolkit for processing, analyzing, and visualizing Electrocardiogram (ECG) signals, with special focus on arrhythmia detection and ST-segment analysis.
This toolkit provides:
- Automated processing of raw ECG signals from MIT-BIH Arrhythmia Database
- Machine learning-powered arrhythmia detection using CNN-LSTM models
- ST-segment analysis for potential heart attack detection
- Interactive visualization tools for clinical and research applications
Key features:
- End-to-end pipeline from raw data to clinical insights
- Modular, object-oriented design for easy extension
- Pre-trained models for immediate use
- Comprehensive visualization capabilities