Combining Optical Character Recognition With Paper ECG Digitization.

Shambavi Ganesh,Pamela T Bhatti,Srini Tridandapani,Mhmtjamil Alkhalaf,Amit J Shah,Shishir Gupta

doi:10.1109/jtehm.2021.3083482

Abstract

Objective: We propose a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open source standalone graphical user interface (GUI) based application. Methods and procedures: To reach this objective we: (1) preprocess the ECG records, which includes skew correction, background grid removal and linear filtering; (2) segment ECG signals using Connected Components Analysis (CCA); (3) implement Optical Character Recognition (OCR) for removal of overlapping ECG lead characters and for interfacing of patients’ demographic information with their research records or their electronic medical record (EMR). The ECG digitization results are validated through a reader study where clinically salient features, such as intervals of QRST complex, between the paper ECG records and the digitized ECG records are compared. Results: Comparison of clinically important features between the paper-based ECG records and the digitized ECG signals, reveals intra- and inter-observer correlations of 0.86–0.99 and 0.79–0.94, respectively. The kappa statistic was found to average at 0.86 and 0.72 for intra- and inter-observer correlations, respectively. Conclusion: The clinically salient features of the ECG waveforms such as the intervals of QRST complex, are preserved during the digitization procedure. Clinical and Healthcare Impact: This open-source digitization tool can be used as a research resource to digitize paper ECG records thereby enabling development of new prediction algorithms to risk stratify individuals with cardiovascular disease, and/or allow for development of ECG-based cardiovascular diagnoses relying upon automated digital algorithms.

Highlights

Electrocardiography (ECG) is a diagnostic tool used by medical professionals to record the electrical activities of the heart [1], and is a critical tool for determining abnormalities of the heart such as cardiac arrhythmias, coronary heart disease, congenital heart defects, and myocardial infarction [2]
We propose the use of Optical Character Recognition (OCR) [15] for removal of overlapping ECG lead characters on the ECG waveforms
The aim of this work was to extract the ECG signals and produce a digitized format from the scanned ECG records, and create an open source standalone executable tool that is independent of equipment vendor

Summary

Introduction

Electrocardiography (ECG) is a diagnostic tool used by medical professionals to record the electrical activities of the heart [1], and is a critical tool for determining abnormalities of the heart such as cardiac arrhythmias, coronary heart disease, congenital heart defects, and myocardial infarction [2]. Supported by ongoing technological advances, patient information is efficiently digitized and stored in their Electronic Medical record (EMR) [3]. While ECG records are increasingly stored in EMRs using digital formats, legacy systems require that ECG records largely continue to be printed on paper for analysis. Digitized ECG records can be utilized by prediction algorithms for further analysis of clinical parameters, and can potentially help contribute to the advancement of current knowledge on clinical diseases [5], [6].

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Results

Discussion

Conclusion