ArticleComputer methods and programs in biomedicine2022
Digitizing ECG image: A new method and open-source software code.
Article in Computer methods and programs in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- ECG-image-database: large-scale paired ECG images and time-series with real-world artifacts; a foundation for computerized ECG digitization and analysis.Physiological measurement · 2026Article
- Direct Reconstruction of High-Fidelity Electrocardiogram Signals From Vector-Based PDF Files With Integrated Deep Learning for Multiparameter Estimation: Retrospective Methodological Study.JMIR formative research · 2026Article
- SynthECG: Python Framework and ECG Image Datasets for Digitization, Lead Detection, and Waveform Segmentation.Journal of medical signals and sensors · 2026Article
- Digitization and Linkage of PDF Formatted 12-Lead Electrocardiograms in Adult Congenital Heart Disease.CJC pediatric and congenital heart disease · 2025Article
- Signal-Guided Multitask Learning for Myocardial Infarction Classification Using Images of Electrocardiogram.Cardiology · 2025Article
- ECG-Image-Kit: a synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization.Physiological measurement · 2024Article
- Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets.Sensors (Basel, Switzerland) · 2024Article
- A fully-automated paper ECG digitisation algorithm using deep learning.Scientific reports · 2022Article
- DNN-derived electrocardiographic age is associated with the atrial fibrillation risk in a Chinese population.Digital healthArticle
- Leveraging ECG images for predicting ejection fraction using machine learning algorithms.Indian heart journalArticle
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Authors and funding
5 authors.
Funding
Abstract
BACKGROUND AND
objectiveWe aimed to develop and validate an open-source code ECG-digitizing tool and assess agreements of ECG measurements across three types of median beats, comprised of digitally recorded simultaneous and asynchronous ECG leads and digitized asynchronous ECG leads.
methodsWe used the data of clinical studies participants (n = 230; mean age 30±15 y; 25% female; 52% had the cardiovascular disease) with available both digitally recorded and printed on paper and then scanned ECGs, split into development (n = 150) and validation (n = 80) datasets. The agreement between ECG and VCG measurements on the digitally recorded time-coherent median beat, representative asynchronous digitized, and digitally recorded beats was assessed by Bland-Altman analysis.
resultsThe sample-per-sample comparison of digitally recorded and digitized signals showed a very high correlation (0.977), a small mean difference (9.3 µV), and root mean squared error (25.9 µV). Agreement between digitally recorded and digitized representative beat was high [area spatial ventricular gradient (SVG) elevation bias 2.5(95% limits of agreement [LOA] -7.9-13.0)°; precision 96.8%; inter-class correlation [ICC] 0.988; Lin's concordance coefficient ρ
conclusionsWe developed and validated an open-source software tool for paper-ECG digitization. Asynchronous ECG leads are the primary source of disagreement in measurements on digitally recorded and digitized ECGs.
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