在心电图流中的异常事件的方法识别:一个系统的映射研究
Uzair Iqbal1, Riyad Almakki2, Muhammad Usman3
1Department of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan. uzair.iqbal@nu.edu.pk.
BMC medical research methodology
|June 4, 2024
概括
本研究审查了用于分类T波变化在心电图 (ECG) 检测心肌梗塞 (MI) 的方法. 它强调了当前的技术,并确定了改进心电图分析的未来研究方向.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
背景情况:
- 电心电图 (ECG) 对于诊断心律失常和心肌梗塞 (MI) 等心脏病至关重要.
- 在心电图中T波异常是评估心脏病发作严重程度的关键指标.
- 现有的研究重点是T波分类,但在综合分析方面仍然存在差距.
研究的目的:
- 系统地审查和综合T波在心电图中的分类,用于MI检测的最先进方法.
- 确定研究问题,并根据该领域的最新进展提供解决方案.
- 突出缺口,并提出未来的研究方向,以加强心电图分析.
主要方法:
- 根据基彻纳姆指南进行了一项系统文献审查 (SLR).
- 这些文章是从IEEE Xplore,Scopus,Science Direct和Springer (2008-2023) 收集的.
- 采用了多层次的过过程,包括关键字搜索,资格标准和专家质量评估.
主要成果:
- 审查确定并讨论了T波分类的各种最先进的方法.
- 这些方法为预先定义的关于通过心电图检测心脏病发作的研究问题提供了解决方案.
- 分析强调了当前在ECG解释中的方法的有效性.
结论:
- 批判性观察揭示了当前T波分类技术中的差距.
- 未来的研究应该专注于ECG的先进特征工程.
- 减小尺寸和理解ECG特征依赖性对于改进分析至关重要.
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