Automatic digitization of paper electrocardiograms - A systematic review.

Alex Lence, Fabrice Extramiana, Ahmad Fall, Joe-Elie Salem, Jean-Daniel Zucker, Edi Prifti

Journal: Journal of electrocardiology 2023;80():125-132

PMID: 37352634

Full Text on Elsevier Science View on PubMed

Abstract

The digitization of electrocardiogram paper records is an essential step to preserve and analyze cardiac data. This digitization process is not flawless as it involves several challenges, such as skew correction, binarization, and signal extraction. Various approaches have been proposed to address these challenges and recent studies have introduced innovative solutions, such as deep learning models and automation processes. Although existing approaches have shown promising results, there is a lack of common databases and metrics where authors could evaluate and compare their methods. Furthermore, the limited accessibility of code or software hinders the comparison process. Overall, while digitization of paper ECG recordings is important in advancing cardiology research, additional efforts are needed to standardize the evaluation process while improving code accessibility. This article provides a systematic review of this process.

Address: IRD, Sorbonne Université, Unité de Modélisation Mathématique et Informatique des Systèmes Complexes, UMMISCO, F-93143 Bondy, France. Electronic address: [email protected].; CRMR Maladies Cardiaques Héréditaires Rares, Hôpital Bichat, Paris, France; Universtité Paris Cité, Paris, France.; IRD, Sorbonne Université, Unité de Modélisation Mathématique et Informatique des Systèmes Complexes, UMMISCO, F-93143 Bondy, France; UMMISCO, Université Cheikh Anta Diop, UCAD, Faculté Des Sciences Et Techniques, FST, ESP, IRD, BP 10700 Dakar, Sénégal.; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Clinical Investigation Center Paris-Est, CIC-1901, INSERM, UNICO-GRECO Cardio-Oncology Program, Department of Pharmacology, Pitié-Salpêtrière University Hospital, Sorbonne, France.; IRD, Sorbonne Université, Unité de Modélisation Mathématique et Informatique des Systèmes Complexes, UMMISCO, F-93143 Bondy, France; Sorbonne Université, INSERM, Nutrition et Obesities; systemic approaches, NutriOmique, AP-HP, Hôpital Pitié-Salpêtrière, France.; IRD, Sorbonne Université, Unité de Modélisation Mathématique et Informatique des Systèmes Complexes, UMMISCO, F-93143 Bondy, France; Sorbonne Université, INSERM, Nutrition et Obesities; systemic approaches, NutriOmique, AP-HP, Hôpital Pitié-Salpêtrière, France. Electronic address: [email protected].

DOI: 10.1016/j.jelectrocard.2023.05.009

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MeSH Terms: Humans, Electrocardiography, Software, Automation, Databases, Factual