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A Systematic Survey of Data Augmentation of ECG Signals for AI Applications

AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strat...

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Detalles Bibliográficos
Autores principales: Rahman, Md Moklesur, Rivolta, Massimo Walter, Badilini, Fabio, Sassi, Roberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256074/
https://www.ncbi.nlm.nih.gov/pubmed/37299964
http://dx.doi.org/10.3390/s23115237
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author Rahman, Md Moklesur
Rivolta, Massimo Walter
Badilini, Fabio
Sassi, Roberto
author_facet Rahman, Md Moklesur
Rivolta, Massimo Walter
Badilini, Fabio
Sassi, Roberto
author_sort Rahman, Md Moklesur
collection PubMed
description AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study’s objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.
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spelling pubmed-102560742023-06-10 A Systematic Survey of Data Augmentation of ECG Signals for AI Applications Rahman, Md Moklesur Rivolta, Massimo Walter Badilini, Fabio Sassi, Roberto Sensors (Basel) Article AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study’s objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring. MDPI 2023-05-31 /pmc/articles/PMC10256074/ /pubmed/37299964 http://dx.doi.org/10.3390/s23115237 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rahman, Md Moklesur
Rivolta, Massimo Walter
Badilini, Fabio
Sassi, Roberto
A Systematic Survey of Data Augmentation of ECG Signals for AI Applications
title A Systematic Survey of Data Augmentation of ECG Signals for AI Applications
title_full A Systematic Survey of Data Augmentation of ECG Signals for AI Applications
title_fullStr A Systematic Survey of Data Augmentation of ECG Signals for AI Applications
title_full_unstemmed A Systematic Survey of Data Augmentation of ECG Signals for AI Applications
title_short A Systematic Survey of Data Augmentation of ECG Signals for AI Applications
title_sort systematic survey of data augmentation of ecg signals for ai applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256074/
https://www.ncbi.nlm.nih.gov/pubmed/37299964
http://dx.doi.org/10.3390/s23115237
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