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Combining Optical Character Recognition With Paper ECG Digitization.

Shambavi Ganesh1, Pamela T Bhatti1, Mhmtjamil Alkhalaf2

  • 1School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGA30332USA.

IEEE Journal of Translational Engineering in Health and Medicine
|July 8, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an open-source MATLAB tool to digitize paper electrocardiography (ECG) records. The tool accurately preserves key ECG waveform features, enabling advanced cardiovascular disease research and diagnosis.

Keywords:
Electrocardiographyconnected component analysiselectronic medical recordoptical character recognition

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Area of Science:

  • Biomedical Engineering
  • Medical Informatics
  • Cardiology

Background:

  • Paper-based electrocardiography (ECG) records pose challenges for digital analysis and research.
  • Digitizing historical ECG data is crucial for leveraging large datasets in cardiovascular research.

Purpose of the Study:

  • To develop a vendor-agnostic, open-source MATLAB tool for converting paper ECGs into digital signals.
  • To ensure the preservation of clinically salient ECG waveform features during digitization.

Main Methods:

  • Preprocessing of ECG records including skew correction and grid removal.
  • Segmentation of ECG signals using Connected Components Analysis (CCA).
  • Optical Character Recognition (OCR) for character removal and data interfacing.

Main Results:

  • High intra- and inter-observer correlations (0.86-0.99 and 0.79-0.94) for digitized ECG features.
  • Average kappa statistics of 0.86 (intra-observer) and 0.72 (inter-observer) indicate reliable digitization.
  • Preservation of clinically important features like QRST complex intervals.

Conclusions:

  • The developed tool successfully digitizes paper ECGs while maintaining waveform integrity.
  • This open-source solution facilitates research in cardiovascular disease risk stratification and automated diagnosis.
  • Enables the use of historical paper ECG data for developing advanced digital algorithms.