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Introduction
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An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
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ECG-Image-Kit: a synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization.

Kshama Kodthalu Shivashankara1, Deepanshi2, Afagh Mehri Shervedani3

  • 1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America.

Physiological Measurement
|August 16, 2024
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Summary

ECG-Image-Kit generates synthetic ECG images for digitizing paper electrocardiograms. This open-source tool aids machine learning in converting scanned ECGs to usable time-series data for improved cardiovascular disease diagnosis.

Keywords:
ECG digitizationdata augmentationdeep learningdenoising CNNelectrocardiogram (ECG)synthetic data

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Area of Science:

  • Biomedical Engineering
  • Computer Science

Background:

  • Cardiovascular diseases are a leading cause of global mortality, with electrocardiograms (ECGs) essential for diagnosis.
  • Traditional printed ECGs and scanned images lack the time-series data required for advanced diagnostic software and machine learning model training.
  • Digitizing historical ECG archives is crucial for advancing AI-driven cardiovascular diagnostics.

Purpose of the Study:

  • To introduce ECG-Image-Kit, an open-source toolbox for generating synthetic multi-lead ECG images with realistic artifacts.
  • To automate the conversion of scanned paper ECGs into usable time-series data points.
  • To facilitate the development of machine learning models for ECG image digitization and classification.

Main Methods:

  • ECG-Image-Kit synthesizes ECG images from real time-series data, incorporating realistic artifacts like text, wrinkles, and creases on ECG paper.
  • A case study generated 21,801 synthetic ECG images from the PhysioNet QT database.
  • A hybrid model combining computer vision and deep neural networks was developed and trained on the synthetic dataset for image-to-time-series conversion.

Main Results:

  • The developed deep learning pipeline accurately digitizes paper ECG images, preserving crucial clinical parameters such as QRS width, RR, and QT intervals.
  • Digitization quality was assessed using signal-to-noise ratio, confirming the pipeline's effectiveness.
  • The study validates a generative approach for ECG image digitization, demonstrating high fidelity with ground truth data.

Conclusions:

  • ECG-Image-Kit provides a robust solution for creating realistic synthetic ECG images, addressing the challenge of limited digitized clinical data.
  • The developed digitization pipeline accurately converts paper ECG images into time-series data, maintaining diagnostic accuracy.
  • This toolbox supports advancements in ECG image digitization, classification, and data augmentation, particularly for challenges like the 2024 PhysioNet Challenge.