ECGWaveform Extractor

This invention, titled ECGWaveform Extractor, provides a novel AI model and preprocessing workflow that enables the extraction of electrocardiogram (ECG) waveform signal data from static, image-based representations of ECG readouts. By applying a series of image denoising, edge detection, and coordinate mapping techniques, this tool transforms analog signals captured in graphical form into digitized point-based CSV outputs.

The model was built and optimized for participation in the Kaggle PhysioNet – Digitization of ECG Images competition, but it has broader utility for:

• Hospitals digitizing paper-based archives
• Remote clinics that capture ECGs as smartphone images
• Researchers building training data for cardiac signal analysis

Authored by Maya P. Brown and team, this work demonstrates a modular approach that can be adapted to other biomedical signals, and includes innovations in visual signal interpretation, file parsing, and reproducible machine learning workflows in Kaggle environments.

🔑 Keywords

• ECG Digitization
• AI for Healthcare
• Biomedical Signal Processing
• ECG Signal Extraction
• Computer Vision in Medicine
• Digital Health Tools
• Kaggle Competition
• PhysioNet Challenge
• Paper to Digital ECG
• Open Source AI

👤 Inventor

Maya P. Brown (Lead Developer, AI Strategist, Visionary)

Optionally include:

• Co-authors or contributors
• GitHub repo link (if you want it public later)
• Kaggle notebook link

📈 Impact Statement

This invention directly contributes to global efforts in digitizing and democratizing access to medical signal data. It has potential implications in underserved communities, disaster zones, and remote diagnostics—where printed ECG images are more accessible than fully digital cardiograph systems.