A fully-automated paper ECG digitisation algorithm using deep learning

A fully-automated paper ECG digitisation algorithm using deep learning

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are curre...

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Bibliographic Details
Published in:Scientific reports Vol. 12; no. 1; pp. 20963 - 12
Main Authors: Wu, Huiyi, Patel, Kiran Haresh Kumar, Li, Xinyang, Zhang, Bowen, Galazis, Christoforos, Bajaj, Nikesh, Sau, Arunashis, Shi, Xili, Sun, Lin, Tao, Yanda, Al-Qaysi, Harith, Tarusan, Lawrence, Yasmin, Najira, Grewal, Natasha, Kapoor, Gaurika, Waks, Jonathan W., Kramer, Daniel B., Peters, Nicholas S., Ng, Fu Siong
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 05-12-2022
Nature Publishing Group
Nature Portfolio
Subjects:
639/705
639/705/258
639/705/794
692/4019
Algorithms
Atrial Fibrillation - diagnosis
Automation
Congestive heart failure
Deep Learning
Digitization
EKG
Electrocardiography
Electrocardiography - methods
Humanities and Social Sciences
Humans
multidisciplinary
Neural networks
Neural Networks, Computer
Science
Science (multidisciplinary)
Segmentation
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Summary:There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60–70% and the average correlation of 3-by-1 ECGs achieved 80–90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-25284-1