High Precision ECG Digitization Using Artificial Intelligence

作者：Anthony Demolder, Viera Krešňáková, Michal Hojcka, Vladimír Boža, Andrej Iring, Adam Rafajdus, Simon Rovder, Timotej Palus, Martin Herman, Felix Bauer, Viktor Jurasek, Róbert Hatala, Jozef Bartúnek, Boris Vavrik, Robert Herman · 发表于：medRxiv · 年份：2024 · DOI：10.1101/2024.08.31.24312876 · 被引用次数：1 · 研究领域：ECG Monitoring and Analysis、Cardiovascular Function and Risk Factors、Cardiac Imaging and Diagnostics

ABSTRACT Background The digitization of electrocardiograms (ECGs) is an important process in modern healthcare, enabling the preservation, transmission, and advanced analysis of ECG data. Traditional methods for digitizing ECGs from paper formats face significant challenges, particularly in real-world scenarios with varying image quality, paper distortions, and overlapping signals. Existing solutions often require manual input and are limited by their dependence on high-quality images and standardized layouts. Methods This study introduces a fully automated, deep learning-based approach for high-precision ECG digitization, imple- menting a two-stage process. In the ECG normalization phase, image distortions are corrected, axes are calibrated, and a standardized grid structure is generated. The ECG reconstruction phase uses deep learning techniques to extract and digitize the leads, with subsequent post-processing to refine the digital signal. The tool was evaluated using a custom-built PMcardio ECG Image Database (PM-ECG-ID) comprising 6,000 ECG images generated from 100 unique ECGs, subjected to various augmentations to simulate real-world challenges. Performance was assessed using Pearson’s correlation coefficient (PCC), root mean squared error (RMSE), and signal-to-noise ratio (SNR). Results The digitization tool demonstrated an average PCC consistently exceeding 91% across all leads, SNR above 12.5 dB and an RMSE below 0.10 mV. The time to ECG digitization was consistentl...