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

Area of Science:

• Cardiology
• Biomedical Engineering
• Data Science

Background:

• Bibliometric analysis is crucial for understanding research impact in specific fields.
• Academic research on arrhythmia detection and classification has grown significantly.
• Evaluating the evolution and trends in this research area is essential.

Purpose of the Study:

• To conduct a bibliometric analysis of research on arrhythmia detection and classification from 2005 to 2022.
• To identify key trends, influential researchers, and leading institutions in the field.
• To map the scientific landscape and future directions of arrhythmia research.

Main Methods:

• Utilized the Web of Science database for publication retrieval.
• Applied the PRISMA 2020 framework for systematic paper selection.
• Employed bibliometric techniques including performance analysis and science mapping.
• Analyzed parameters such as publication counts, citation analysis, and co-authorship networks.

Main Results:

• 238 publications were selected for analysis.
• China, the USA, and India emerged as the top countries in publications and citations.
• U. R. Acharya, S. Dogan, and P. Plawiak were identified as highly cited researchers.
• Machine learning, ECG, and deep learning were the most frequent keywords, with machine learning, ECG, and atrial fibrillation being popular topics.

Conclusions:

• The study provides a comprehensive overview of the research landscape in arrhythmia detection and classification.
• It highlights the dominant contributions of specific countries and researchers.
• Identified key technologies and research themes, offering insights into the field's trajectory.