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 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 (OCR) 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 paper ECG. There was 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 approximately 0.8-0.9 (p<0.05), and kappa statistics ranging from 0.85 (inter-observer) to 1.00 (intra-observer).
Conclusions: 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.
See complete bios of the authors in the full version of this article.
Mr. Ravichandran was a postdoctoral fellow in the Department of Radiology and Imaging Sciences at Emory University, Atlanta, from 2011 to 2012. His primary research interests are in the areas of development of signal and image processing algorithms, analysis of biomedical data and, architectural implementation of signal processing algorithms.
Mr. Harless is currently seeking a B.S. degree in electrical engineering from the Georgia Institute of Technology, Atlanta, GA. His main areas of research interest are signal processing, embedded systems, and biotechnology.
Dr. Shah will complete his clinical cardiology training in 2013 at Emory University and also has an M.S. in Clinical Research. He is the recipient of many institutional and international research awards, including the Curtis Carl Johnson Award for the best student poster presentation at the18th Annual International Bioelectromagnetics Society conference.
Dr. McClellan has been a Professor in the School of Electrical and Computer Engineering at Georgia Tech, where he presently holds the John and Marilu McCarty Chair. He is a co-author of the texts Number Theory in Digital Signal Processing, Computer Exercises for Signal Processing, DSP First: A Multimedia Approach, and Signal Processing First.
Mr. Wick is currently pursuing a Ph.D. from the Georgia Institute of Technology. His current research is focused on digital signal processing of cardiac signals, with applications for motion analysis and tracking.
Dr. Tridandapani is a board-certified radiologist, and is a faculty member in the Department of Radiology and Imaging Sciences at Emory University and an Adjunct Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. His research involves the development of novel gating strategies for optimizing cardiac computed tomography and tools to increase patient safety in medical imaging.
The issue of electronic health records is very significant in society to provide better healthcare and associated services. This paper addresses a crucial need for digitization of the enormous number of ECG records in order for them to be clinically useful in an EHR/EMR system. Such records will be useful for better healthcare services but also to mine the data for clinical studies and risk management protocols.
This article appeared in the 2013 issue of IEEE Journal of Translational Engineering in Health and Medicine.
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