Literature DB >> 16216602

ECGScan: a method for conversion of paper electrocardiographic printouts to digital electrocardiographic files.

Fabio Badilini1, Tanju Erdem, Wojciech Zareba, Arthur J Moss.   

Abstract

BACKGROUND: Measurements of parameters from electrocardiograms (ECGs) are still largely performed from paper ECG records. Recent guidelines from regulatory agencies and, in particular, the requirement of the Food and Drug Administration to enforce the digital submission of annotated ECGs have triggered significant efforts in the pharmaceutical industry, which, to comply with the new guidelines, is adopting digital ECG technology. At the same time, the new requirements justify the need for tools to convert existing paper ECG records into digital format, particularly for retrospective studies.
METHODS: This article presents ECGScan, a computer application developed for the conversion of paper ECG records to digital ECG files. An image processing engine is used to first detect the underlying grid and, subsequently, to extrapolate the ECG waveforms using a technique based on active contour modeling.
RESULTS: ECGScan was validated using a set of 60 ECGs for which both the original digital waveform and paper printouts were available. Sample-by-sample comparisons provided evidence of a robust wave reconstruction (root mean square value from 169 PQRST complexes was 16.8+/-11.8 microV). Semiautomatic measurements of QT intervals performed on 144 complexes also indicated a strong agreement between original and derived ECGs (DeltaQT=0.577+/-5.41 milliseconds).
CONCLUSIONS: ECGScan provides a robust reconstruction of a digital ECG, both in waveform reconstruction and in QT measurements performed on original (digital) ECGs and on digitized ECGs from paper printouts.

Mesh:

Year:  2005        PMID: 16216602     DOI: 10.1016/j.jelectrocard.2005.04.003

Source DB:  PubMed          Journal:  J Electrocardiol        ISSN: 0022-0736            Impact factor:   1.438


  14 in total

1.  High Precision Digitization of Paper-Based ECG Records: A Step Toward Machine Learning.

Authors:  Mohammed Baydoun; Lise Safatly; Ossama K Abou Hassan; Hassan Ghaziri; Ali El Hajj; Hussain Isma'eel
Journal:  IEEE J Transl Eng Health Med       Date:  2019-11-07       Impact factor: 3.316

2.  Marked exercise-induced T-wave heterogeneity in symptomatic diabetic patients with nonflow-limiting coronary artery stenosis.

Authors:  Fernando G Stocco; Ederson Evaristo; Nishant R Shah; Michael K Cheezum; Jon Hainer; Courtney Foster; Bruce D Nearing; Ernest Gervino; Richard L Verrier
Journal:  Ann Noninvasive Electrocardiol       Date:  2017-09-26       Impact factor: 1.468

3.  T-wave morphology abnormalities in benign, potent, and arrhythmogenic I(kr) inhibition.

Authors:  Jean-Philippe Couderc; Xiajuan Xia; Derick R Peterson; Scott McNitt; Hongwei Zhao; Slava Polonsky; Arthur J Moss; Wojciech Zareba
Journal:  Heart Rhythm       Date:  2011-02-09       Impact factor: 6.343

4.  Ranolazine reduces repolarization heterogeneity in symptomatic patients with diabetes and non-flow-limiting coronary artery stenosis.

Authors:  Ederson Evaristo; Fernando G Stocco; Nishant R Shah; Michael K Cheezum; Jon Hainer; Courtney Foster; Bruce D Nearing; Marcelo Di Carli; Richard L Verrier
Journal:  Ann Noninvasive Electrocardiol       Date:  2017-06-27       Impact factor: 1.468

5.  ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration.

Authors:  Omneya Attallah
Journal:  Comput Biol Med       Date:  2022-01-05       Impact factor: 4.589

6.  Digitizing ECG image: A new method and open-source software code.

Authors:  Julian D Fortune; Natalie E Coppa; Kazi T Haq; Hetal Patel; Larisa G Tereshchenko
Journal:  Comput Methods Programs Biomed       Date:  2022-05-14       Impact factor: 7.027

7.  An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.

Authors:  Omneya Attallah
Journal:  Biosensors (Basel)       Date:  2022-05-05

8.  An electrocardiographic sign of ischemic preconditioning.

Authors:  Loek P B Meijs; Loriano Galeotti; Esther P Pueyo; Daniel Romero; Robert B Jennings; Michael Ringborn; Stafford G Warren; Galen S Wagner; David G Strauss
Journal:  Am J Physiol Heart Circ Physiol       Date:  2014-04-28       Impact factor: 4.733

9.  Novel Tool for Complete Digitization of Paper Electrocardiography Data.

Authors:  Lakshminarayan Ravichandran; Chris Harless; Amit J Shah; Carson A Wick; James H Mcclellan; Srini Tridandapani
Journal:  IEEE J Transl Eng Health Med       Date:  2013       Impact factor: 3.316

10.  Combining Optical Character Recognition With Paper ECG Digitization.

Authors:  Shambavi Ganesh; Pamela T Bhatti; Mhmtjamil Alkhalaf; Shishir Gupta; Amit J Shah; Srini Tridandapani
Journal:  IEEE J Transl Eng Health Med       Date:  2021-05-25       Impact factor: 3.316

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