Lakshminarayan Ravichandran1, Chris Harless2, Amit J Shah3, Carson A Wick2, James H Mcclellan2, Srini Tridandapani4. 1. Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30322, USA. 2. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. 3. Department of Medicine-Cardiology, Emory University, Atlanta, GA 30322. 4. Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30322, USA, and also with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
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 (p < 0.05), 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.
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 (p < 0.05), 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.
Entities:
Keywords:
Digitization; electrocardiography; electronic medical records; optical character recognition
Authors: Amit Jasvant Shah; Shaoyong Su; Emir Veledar; James Douglas Bremner; Felicia C Goldstein; Rachel Lampert; Jack Goldberg; Viola Vaccarino Journal: Psychosom Med Date: 2011-06-28 Impact factor: 4.312
Authors: Zhu-Ming Zhang; Ronald J Prineas; Douglas Case; Elsayed Z Soliman; Pentti M Rautaharju Journal: Am J Cardiol Date: 2007-06-18 Impact factor: 2.778
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
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