ArticleIEEE journal of translational engineering in health and medicine2019
High Precision Digitization of Paper-Based ECG Records: A Step Toward Machine Learning.
Article in IEEE journal of translational engineering in health and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review.Frontiers in medicine · 2023Pooled it
- 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
- GenECG: a synthetic image-based ECG dataset to augment artificial intelligence-enhanced algorithm development.BMJ health & care informatics · 2025Article
- Paper-Recorded ECG Digitization Method with Automatic Reference Voltage Selection for Telemonitoring and Diagnosis.Diagnostics (Basel, Switzerland) · 2024Article
- ECG-Image-Kit: a synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization.Physiological measurement · 2024Article
- A fully-automated paper ECG digitisation algorithm using deep learning.Scientific reports · 2022Article
- A multi-label classification system for anomaly classification in electrocardiogram.Health information science and systems · 2022Article
- Artificial intelligence to detect abnormal heart rhythm from scanned electrocardiogram tracings.Journal of arrhythmia · 2022Article
- Digitizing ECG image: A new method and open-source software code.Computer methods and programs in biomedicine · 2022Article
- Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning.BMC medical informatics and decision making · 2021Article
- Combining Optical Character Recognition With Paper ECG Digitization.IEEE journal of translational engineering in health and medicine · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form.
objectivesWe present a MATLAB-based tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal.
methods30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results.
resultsThe validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/-0.021 (p-value < 0.001), QRS interval 1+/- SD (p-value < 0.001), QT interval 0.981 +/- 0.023 p-value < 0.001, and RR interval 1 +/- 0.001 p-value < 0.001.
conclusionDigitized ECG signals from existing paper or scanned ECGs can be obtained with more than 95% of precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnostic and prognostic evaluation of patients with cardiovascular disease.
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