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

Abstract

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.

Heart Rate Variability Analysis of Healthy Individuals and Patients with Ischemia and Arrhythmia

“…The research conducted and the results presented show reduced values of heart rate variability indicators in patients with arrhythmia and IHD compared to healthy people. In the time domain, the parameters SDNN, SDANN, RMSSD, pNN50 and the HRV triangular index were lower in the examined cardiac data of diseased subjects compared to healthy subjects and were significantly lower than their respective reference values (presented in [5]).…”

“…Of the cardiovascular diseases, arrhythmia is one of the most critical conditions [5] of the heart, for which a physiological rationale continues to be sought [6]. Several million people suffer from arterial fibrillation in the United States, and their number is constantly increasing [7,8], as is the mortality from this condition [9].…”

Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research

“…Regarding the countries that have been listed as leading contributors, in our case it has been observed that India, China, and the USA are the top contributors. It was observed that these countries are also found in top-contributors lists for other bibliometric studies existing in the academic literature, such as the ones in the areas of opinion mining and sentiment analysis [68], COVID-19 vaccination misinformation [59], social media research in times of COVID-19 [58], social media research in the age of COVID-19 [58], health-related misinformation in social media [69], text mining and maintenance [67], classifications of artificial intelligence using convolutional neural networks [65], sentiment analysis in times of COVID-19 [12], and arrhythmia detection and classification [70]. As a result of these observations, it can be highlighted, once more, the important contribution of these countries to the body of research.…”

A Bibliometric Analysis of the Relationship Between Workforce Diversity and Innovation

“…For the analysis and visualization of the data resulted from the exported database, besides Microsoft Excel, two popular software were used: Biblioshiny, within RStudio's Bibliometrix package (Aria & Cuccurullo, 2017) and VOSviewer . Biblioshiny has a user-friendly interface, allowing an easy data import, modification, as well as interactive visualization; data can be visualized as graphs, line plots, three field plots, maps, and networks which facilitate the research undertaking (Gronthy et al, 2023) As can be observed in Figure 1, the amount of academic production on the link between workforce diversity and innovation was almost absent before 2009, but has significantly increased after this year, with the highest number of publications in 2022 reaching a total of sixty-seven. 2023 and 2021 were also very productive years regarding this topic with a number of thirty-nine and respectively thirtyeight papers per year, followed by 2017 and 2019 with an annual number of twenty-nine articles, and 2020 which produced around twenty-five papers per year.…”