在心电成像中对计算技术,数据集利用和特征提取的系统审查
Dagoberto Mayorca-Torres1,2, Alejandro J León-Salas3, Diego H Peluffo-Ordoñez4,5,6
1Department of Software Systems and Programming Languages, Universidad de Granada, C/Periodista Daniel Saucedo Aranda s/n, Granada, 18071, Spain. dago.mayorca.torres@gmail.com.
Medical & biological engineering & computing
|January 8, 2025
概括
心电图成像 (ECGI) 重建中的计算技术正在发展. 传统方法仍然是首选的,但像深度学习这样的先进技术越来越多地用于精确的心脏诊断和治疗.
科学领域:
- 生物医学工程 生物医学工程
- 医疗成像医学成像
- 计算科学 计算科学
背景情况:
- 电心图像 (ECGI) 重建对于非侵入性心脏评估至关重要.
- 了解所使用的计算技术对于提高ECGI准确性至关重要.
- 需要对最新文献进行系统审查,以确定当前的趋势和差距.
研究的目的:
- 分析ECGI重建中使用的计算技术.
- 识别数据集使用趋势,解决问题的方法和特征提取.
- 突出ECGI的进展和未来的研究方向.
主要方法:
- 使用PRISMA指南进行系统的文献审查.
- 在Scopus和Web of Science数据库中进行的搜索.
- 考克莱恩原则应用于偏见风险评估.
主要成果:
- 分析了2010年至2023年间发表的99篇同行评审的英语论文.
- 传统方法的主导地位 (边界元素,提霍诺夫).
- 在ECGI中,混合技术和深度学习的新兴趋势.
结论:
- 计算ECGI的进步提高了心脏诊断和治疗精度.
- 在ECGI中需要强大的数据利用和创新的计算集成.
- 在诊断和治疗方法方面取得了重大进展,以实现量身定制的心脏护理.
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