title: 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
  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).
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30322, USA.
authors.forename: Lakshminarayan
authors.initials: L
authors.lastname: Ravichandran
authors.affiliation: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
authors.forename: Chris
authors.initials: C
authors.lastname: Harless
authors.affiliation: Department of Medicine-Cardiology, Emory University, Atlanta, GA 30322.
authors.forename: Amit J
authors.initials: AJ
authors.lastname: Shah
authors.affiliation: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
authors.forename: Carson A
authors.initials: CA
authors.lastname: Wick
authors.affiliation: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
authors.forename: James H
authors.initials: JH
authors.lastname: Mcclellan
authors.affiliation: 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.
authors.forename: Srini
authors.initials: S
authors.lastname: Tridandapani
keywords: Digitization; electrocardiography; electronic medical records; optical character recognition