Novel Tool for Complete Digitization of Paper Electrocardiography Data

Abstract

Objective: We present a Matlab-based tool to convert electrocardiography (ECG) information from paper charts into digital ECG signals. The tool can be used for long-term retrospective studies of cardiac patients to study the evolving features with prognostic value. Methods and procedures: To perform the conversion, we: 1) detect the graphical grid on ECG charts using grayscale thresholding; 2) digitize the ECG signal based on its contour using a column-wise pixel scan; and 3) use template-based optical character recognition to extract patient demographic information from the paper ECG in order to interface the data with the patients' medical record. To validate the digitization technique: 1) correlation between the digital signals and signals digitized from paper ECG are performed and 2) clinically significant ECG parameters are measured and compared from both the paper-based ECG signals and the digitized ECG. Results: The validation demonstrates a correlation value of 0.85–0.9 between the digital ECG signal and the signal digitized from the paper ECG. There is a high correlation in the clinical parameters between the ECG information from the paper charts and digitized signal, with intra-observer and inter-observer correlations of 0.8–0.9 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$({\rm p}<{0.05})$\end{document}, and kappa statistics ranging from 0.85 (inter-observer) to 1.00 (intra-observer). Conclusion: The important features of the ECG signal, especially the QRST complex and the associated intervals, are preserved by obtaining the contour from the paper ECG. The differences between the measures of clinically important features extracted from the original signal and the reconstructed signal are insignificant, thus highlighting the accuracy of this technique. Clinical impact: Using this type of ECG digitization tool to carry out retrospective studies on large databases, which rely on paper ECG records, studies of emerging ECG features can be performed. In addition, this tool can be used to potentially integrate digitized ECG information with digital ECG analysis programs and with the patient's electronic medical record.

Experiences in digitizing and digitally measuring a paper-based ECG archive

“…Different studies also applied different statistical validation methods, ranging from a comparison of interval lengths and amplitudes [13][14][15][16] to comparisons of areas and signal overlays [13,17]. Most studies used only the leads with the most distinguishable complexes for the validation [13,15,17,18] or studied only specific parameters [17]. In the present study, all basic intervals from all basic 12 leads as well as the orthogonal X, Y, and Z leads were used in the validation, in addition to the R, S, and T wave amplitudes from the representative leads.…”

P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

“…These paper-based ECG images require digitization so that they can be transformed into 1D signals in order to apply the state-of-the-art algorithms that have been well studied in the literature. Working on a digitized version of paper-based ECGs (1D version) has important advantages in terms of storage space and the ease of exchanging patients' clinical information between healthcare facilities [2,4,5]. Yet, certain limitations remain, such as dealing with alignment issues, the loss of relative timing between leads, and inaccuracy in high-frequency regions, to name of few.…”

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

“…Several methods are suggested in the literature to achieve acceptable digitization accuracy. Ravichandran et al used a Matlab-based tool that is based on the contour detection of the ECG signal after removing the background grid through thresholding [5]. Their work achieves batch processing of multiple ECGs by utilizing Optical Character Recognition (OCR) to extract the medical information of the patient that is available in the scanned record.…”