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A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022

Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to...

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Detalles Bibliográficos
Autores principales: Gronthy, Ummay Umama, Biswas, Uzzal, Tapu, Salauddin, Samad, Md Abdus, Nahid, Abdullah-Al
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217318/
https://www.ncbi.nlm.nih.gov/pubmed/37238216
http://dx.doi.org/10.3390/diagnostics13101732
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author Gronthy, Ummay Umama
Biswas, Uzzal
Tapu, Salauddin
Samad, Md Abdus
Nahid, Abdullah-Al
author_facet Gronthy, Ummay Umama
Biswas, Uzzal
Tapu, Salauddin
Samad, Md Abdus
Nahid, Abdullah-Al
author_sort Gronthy, Ummay Umama
collection PubMed
description Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to 2022. We have followed PRISMA 2020 framework to identify, filter and select the relevant papers. This study has used the Web of Science database to find related publications on arrhythmia detection and classification. “Arrhythmia detection”, “arrhythmia classification” and “arrhythmia detection and classification” are three keywords for gathering the relevant articles. 238 publications in total were selected for this research. In this study, two different bibliometric techniques, “performance analysis” and “science mapping”, were applied. Different bibliometric parameters such as publication analysis, trend analysis, citation analysis, and networking analysis have been used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations are China, the USA, and India in terms of arrhythmia detection and classification. The three most significant researchers in this field are those named U. R. Acharya, S. Dogan, and P. Plawiak. Machine learning, ECG, and deep learning are the three most frequently used keywords. A further finding of the study indicates that the popular topics for arrhythmia identification are machine learning, ECG, and atrial fibrillation. This research provides insight into the origins, current status, and future direction of arrhythmia detection research.
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spelling pubmed-102173182023-05-27 A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022 Gronthy, Ummay Umama Biswas, Uzzal Tapu, Salauddin Samad, Md Abdus Nahid, Abdullah-Al Diagnostics (Basel) Review Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to 2022. We have followed PRISMA 2020 framework to identify, filter and select the relevant papers. This study has used the Web of Science database to find related publications on arrhythmia detection and classification. “Arrhythmia detection”, “arrhythmia classification” and “arrhythmia detection and classification” are three keywords for gathering the relevant articles. 238 publications in total were selected for this research. In this study, two different bibliometric techniques, “performance analysis” and “science mapping”, were applied. Different bibliometric parameters such as publication analysis, trend analysis, citation analysis, and networking analysis have been used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations are China, the USA, and India in terms of arrhythmia detection and classification. The three most significant researchers in this field are those named U. R. Acharya, S. Dogan, and P. Plawiak. Machine learning, ECG, and deep learning are the three most frequently used keywords. A further finding of the study indicates that the popular topics for arrhythmia identification are machine learning, ECG, and atrial fibrillation. This research provides insight into the origins, current status, and future direction of arrhythmia detection research. MDPI 2023-05-13 /pmc/articles/PMC10217318/ /pubmed/37238216 http://dx.doi.org/10.3390/diagnostics13101732 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 Review
Gronthy, Ummay Umama
Biswas, Uzzal
Tapu, Salauddin
Samad, Md Abdus
Nahid, Abdullah-Al
A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022
title A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022
title_full A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022
title_fullStr A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022
title_full_unstemmed A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022
title_short A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022
title_sort bibliometric analysis on arrhythmia detection and classification from 2005 to 2022
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217318/
https://www.ncbi.nlm.nih.gov/pubmed/37238216
http://dx.doi.org/10.3390/diagnostics13101732
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