合成心电图信号生成:一个范围审查
Beatrice Zanchi1, Giuliana Monachino2, Luigi Fiorillo3
1Institute of Digital Technologies for Personalized Healthcare MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Via la Santa 1, Lugano, 6900, Switzerland; Department of Quantitative Biomedicine, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland.
Computers in biology and medicine
|November 29, 2024
概括
生成合成心电图 (ECG) 数据对于推进心脏研究和开放科学至关重要. 本综述分析了方法,帮助研究人员选择适当的技术来创建高质量的合成心电图信号.
科学领域:
- 生物医学工程 生物医学工程
- 计算心脏病学 计算心脏病学
- 数据科学数据科学数据科学
背景情况:
- 科学界对合成心电图 (ECG) 数据生成日益感兴趣.
- 合成心电图信号为了解心脏电活动,创建公正的数据集,并为开放科学提供数据匿名化提供了好处.
- 需要强大的方法来生成现实的和多样化的合成心电图数据至关重要.
研究的目的:
- 对生成合成心电图数据的方法进行范围审查.
- 分析各种合成心电图数据生成技术的局限性和可能性.
- 为选择最适合特定应用的技术提供指导.
主要方法:
- 对79项相关研究进行系统的文献搜索和分析.
- 研究的分类基于方法 (数学建模,计算机视觉,深度生成模型),线索数,心跳数和数据合成目的.
- 确定方法的优点和弱点的定性评估.
主要成果:
- 确定了三种合成心电图数据生成方法的主要类别:数学建模,基于计算机视觉的方法和深度生成模型.
- 综述强调了不同合成心电图生成技术的多样化应用以及不同程度的真实性和可变性.
- 一个重大挑战在于建立标准化指标来定量评估合成心电图数据的质量.
结论:
- 该分析提供了全面的概述,以帮助研究人员选择合适的合成心电图数据生成方法.
- 需要进一步的研究来开发合成心电图数据的标准化评估指标.
- 合成心电图生成的进步对于加速心脏电生理学研究和改进数据共享实践至关重要.
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