Paper-Recorded ECG Digitization Method with Automatic Reference Voltage Selection for Telemonitoring and Diagnosis

• Main Authors: Wang, Liang Hung, Xie, Chao Xin, Yang, Tao, Tan, Hong Xin, Fan, Ming Hui, Kuo, I. Chun, Lee, Zne Jung, Chen, Tsung Yi, Huang, Pao Cheng, Chen, Shih Lun, Abu, Patricia Angela R
• Format: text
• Published: Archīum Ateneo 2024
• Subjects: ECG data recovery; ECG signal extraction; image distortion correction; signal reconstruction; uneven light correction; Biomedical; Biomedical Engineering and Bioengineering; Computer Engineering; Computer Sciences; Electrical and Computer Engineering; Engineering; Medicine and Health Sciences; Physical Sciences and Mathematics
• Online Access: https://archium.ateneo.edu/discs-faculty-pubs/422; https://archium.ateneo.edu/context/discs-faculty-pubs/article/1424/viewcontent/diagnostics_14_01910.pdf
• Institution: Ateneo De Manila University

In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground–background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed to ensure consistency in amplitude, rather than solely in shape, for digital signals. The original signal reconstruction algorithm was validated with the MIT–BIH and PTB databases by comparing the difference between the reconstructed and the original signals. Moreover, the mean of the Pearson correlation coefficient was 0.97 and 0.98, respectively, while the mean absolute errors were 0.324 and 0.241, respectively. The method proposed in this study converts paper-recorded ECGs into a digital format, enabling direct analysis using software. Automated techniques for acquiring and restoring ECG reference voltages enhance the reconstruction accuracy. This innovative approach facilitates data storage, medical communication, and remote ECG analysis, and minimizes errors in remote diagnosis.