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.

中文翻译：

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将光学字符识别与纸质心电图数字化相结合


目标：我们提出了一种基于 MATLAB 的工具，用于将心电图 (ECG) 波形从纸质心电图记录转换为与供应商无关的数字化心电图信号。该工具被打包为基于开源独立图形用户界面 (GUI) 的应用程序。方法和程序：为了达到这一目标，我们：（1）对心电图记录进行预处理，包括倾斜校正、背景网格去除和线性滤波； (2) 使用连通分量分析 (CCA) 对心电图信号进行分段； (3) 实施光学字符识别 (OCR)，以删除重叠的心电图导联字符，并将患者的人口统计信息与其研究记录或电子病历 (EMR) 连接起来。心电图数字化结果通过读者研究进行验证，其中比较纸质心电图记录和数字化心电图记录之间的临床显着特征，例如 QRST 复合波的间隔。结果：纸质心电图记录和数字化心电图信号之间的临床重要特征的比较显示，观察者内和观察者间的相关性分别为 0.86-0.99 和 0.79-0.94。研究发现观察者内和观察者间相关性的 kappa 统计量平均值分别为 0.86 和 0.72。结论：在数字化过程中保留了心电图波形的临床显着特征，例如 QRST 复合波的间隔。临床和医疗保健影响：这种开源数字化工具可用作研究资源，将纸质心电图记录数字化，从而能够开发新的预测算法，对患有心血管疾病的个体进行风险分层，和/或允许开发基于心电图的心血管诊断依靠自动化数字算法。