Deep learning for digitizing highly noisy paper-based ECG records

Yao, Li; Qu, Qixun; Wang, Meng; Yu, Liheng; Wang, Jun; Shen, Linghao; He, Kunlun

doi:10.1016/j.compbiomed.2020.104077

Cited by 24 publications

(11 citation statements)

References 11 publications

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“…Paradoxically, while we are already in the digital era, in several use cases, these "ECG images" need to be processed shortly after being printed, to recover the "voltage-versustime" nature of the signals [104][105][106] (this was the subject of P.R. 's PhD thesis in the late 1960s!…”

Section: Discussionmentioning

confidence: 99%

“…), either for research purposes, for submitting annotated digital ECGs to the FDA for drug approval [104], or for computing new ECG measurements. Some authors claim that by using this "reverse-printing" process they can achieve (only) up to 97% digitization accuracy [105], a figure that does not guarantee that the reconstructed signal will allow detecting small Q waves as defined in [13]. Other AI researchers further argue that the original ECG signals are not really needed as AI-based ECG interpretation may be performed by directly processing the "ECG images", just as any medical image record [103,106,107].…”

Section: Discussionmentioning

confidence: 99%

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The History and Challenges of SCP-ECG: The Standard Communication Protocol for Computer-Assisted Electrocardiography

Rubel

Fayn

Macfarlane

et al. 2021

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Ever since the first publication of the standard communication protocol for computer-assisted electrocardiography (SCP-ECG), prENV 1064, in 1993, by the European Committee for Standardization (CEN), SCP-ECG has become a leading example in health informatics, enabling open, secure, and well-documented digital data exchange at a low cost, for quick and efficient cardiovascular disease detection and management. Based on the experiences gained, since the 1970s, in computerized electrocardiology, and on the results achieved by the pioneering, international cooperative research on common standards for quantitative electrocardiography (CSE), SCP-ECG was designed, from the beginning, to empower personalized medicine, thanks to serial ECG analysis. The fundamental concept behind SCP-ECG is to convey the necessary information for ECG re-analysis, serial comparison, and interpretation, and to structure the ECG data and metadata in sections that are mostly optional in order to fit all use cases. SCP-ECG is open to the storage of the ECG signal and ECG measurement data, whatever the ECG recording modality or computation method, and can store the over-reading trails and ECG annotations, as well as any computerized or medical interpretation reports. Only the encoding syntax and the semantics of the ECG descriptors and of the diagnosis codes are standardized. We present all of the landmarks in the development and publication of SCP-ECG, from the early 1990s to the 2009 International Organization for Standardization (ISO) SCP-ECG standards, including the latest version published by CEN in 2020, which now encompasses rest and stress ECGs, Holter recordings, and protocol-based trials.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The History and Challenges of SCP-ECG: The Standard Communication Protocol for Computer-Assisted Electrocardiography

Rubel

Fayn

Macfarlane

et al. 2021

Hearts

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“…In this section, the conversion of noised 12-lead paper-based ECG images to noiseless channel-based binary images is explained. There are many studies that digitize paper-based ECG images [ 55 , 56 ]. Nevertheless, these studies have high computational costs.…”

Section: Methodsmentioning

confidence: 99%

Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning

Özdemir

Güren

2021

BMC Med Inform Decis Mak

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Background Coronavirus disease 2019 (COVID-19) has become a pandemic since its first appearance in late 2019. Deaths caused by COVID-19 are still increasing day by day and early diagnosis has become crucial. Since current diagnostic methods have many disadvantages, new investigations are needed to improve the performance of diagnosis. Methods A novel method is proposed to automatically diagnose COVID-19 by using Electrocardiogram (ECG) data with deep learning for the first time. Moreover, a new and effective method called hexaxial feature mapping is proposed to represent 12-lead ECG to 2D colorful images. Gray-Level Co-Occurrence Matrix (GLCM) method is used to extract features and generate hexaxial mapping images. These generated images are then fed into a new Convolutional Neural Network (CNN) architecture to diagnose COVID-19. Results Two different classification scenarios are conducted on a publicly available paper-based ECG image dataset to reveal the diagnostic capability and performance of the proposed approach. In the first scenario, ECG data labeled as COVID-19 and No-Findings (normal) are classified to evaluate COVID-19 classification ability. According to results, the proposed approach provides encouraging COVID-19 detection performance with an accuracy of 96.20% and F1-Score of 96.30%. In the second scenario, ECG data labeled as Negative (normal, abnormal, and myocardial infarction) and Positive (COVID-19) are classified to evaluate COVID-19 diagnostic ability. The experimental results demonstrated that the proposed approach provides satisfactory COVID-19 prediction performance with an accuracy of 93.00% and F1-Score of 93.20%. Furthermore, different experimental studies are conducted to evaluate the robustness of the proposed approach. Conclusion Automatic detection of cardiovascular changes caused by COVID-19 can be possible with a deep learning framework through ECG data. This not only proves the presence of cardiovascular changes caused by COVID-19 but also reveals that ECG can potentially be used in the diagnosis of COVID-19. We believe the proposed study may provide a crucial decision-making system for healthcare professionals. Source code All source codes are made publicly available at: https://github.com/mkfzdmr/COVID-19-ECG-Classification

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“…Several other algorithms for paper ECG digitization have been previously developed. [6,7,[30][31][32][33][34][35][36][37][38] Unlike several earlier digitizing applications that scaled-down image resolution to reduce the computational cost, [7,31] we elected to use high-resolution images (600 dpi), and output All rights reserved. No reuse allowed without permission.…”

Section: Comparison Of the Paper-ecg Digitization Methodsmentioning

confidence: 99%

Digitizing ECG image: new fully automated method and open-source software code

Fortune

Coppa

Haq

et al. 2021

Preprint

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Background: We aimed to develop and validate an automated, 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. Methods: We 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. Results: 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) degrees; precision 96.8%; inter-class correlation [ICC] 0.988; Lin concordance coefficient ρc 0.97(95% confidence interval [CI] 0.95-0.98)]. Agreement between digitally recorded asynchronous and time-coherent median beats was moderate for area-based VCG metrics (spatial QRS-T angle bias 1.4(95%LOA -33.2-30.3) degrees; precision 94.8%; ICC 0.95; Lin concordance coefficient ρc 0.90(95%CI 0.82-0.95)], but poor for peak-based VCG metrics of global electrical heterogeneity. Conclusions: We 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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Deep learning for digitizing highly noisy paper-based ECG records

Cited by 24 publications

References 11 publications

The History and Challenges of SCP-ECG: The Standard Communication Protocol for Computer-Assisted Electrocardiography

The History and Challenges of SCP-ECG: The Standard Communication Protocol for Computer-Assisted Electrocardiography

Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning

Digitizing ECG image: new fully automated method and open-source software code

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