从2005年到2022年对心律失常检测和分类的图书统计分析
Ummay Umama Gronthy1, Uzzal Biswas1, Salauddin Tapu1
1Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.
Diagnostics (Basel, Switzerland)
|May 27, 2023
概括
这份从2005年至2022年间对心律失常检测和分类研究的文献分析显示,中国,美国和印度是主要贡献者. 关键主题包括机器学习,心电图和心房动.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 图书统计分析对于理解特定领域的研究影响至关重要.
- 对心律失常检测和分类的学术研究已经显著增长.
- 评估这一研究领域的演变和趋势至关重要.
研究的目的:
- 对2005年至2022年间关于心律失常检测和分类的研究进行文献计量分析.
- 识别关键趋势,有影响力的研究人员和该领域的领先机构.
- 绘制科学格局和心律失常研究的未来方向.
主要方法:
- 使用 Web of Science 数据库进行出版物检索.
- 应用PRISMA 2020框架进行系统的论文选择.
- 采用文献计量技术,包括绩效分析和科学映射.
- 分析了诸如出版数量,引用分析和共同作者网络等参数.
主要成果:
- 238个出版物被选中进行分析.
- 中国,美国和印度成为出版物和引文排名第一的国家.
- 乌.R.阿查利亚,S.多根和P.Plawiak被确定为被引用的研究人员.
- 机器学习,心电图和深度学习是最常见的关键词,机器学习,心电图和心房动是最受欢迎的主题.
结论:
- 这项研究提供了关于心律失常检测和分类的研究格局的全面概述.
- 它强调了特定国家和研究人员的主要贡献.
- 确定了关键技术和研究主题,为该领域的发展轨迹提供了洞察力.
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