信号处理框架用于检测心室外宫内节拍发作
Avvaru Srinivasulu1,2,3, Natarajan Sriraam4
1Research Scholar, Center for Medical Electronics and Computing, M.S Ramaiah Institute of Technology, Belgaum, Karnataka, India.
Journal of medical signals and sensors
|August 25, 2023
概括
这项研究引入了一种自动信号处理框架,用于在长期心电图 (ECG) 数据中检测心室外心跳 (VEB) 情节,显著减少手动分析时间并提高诊断效率.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 信号处理 信号处理
背景情况:
- 长期心电图 (ECG) 分析以检测心脏异常,如心室外拍 (VEBs) 是耗时的,因为手动交叉检查.
- 现有的霍尔特监视器技术捕获了心电图数据,但依赖于手动解释,在诊断中造成了瓶.
研究的目的:
- 开发和验证一种有效的自动心脏病发作检测技术,以减少分析长期心电图信号的手工负担.
- 介绍一个信号处理框架,用于在不同数据库中检测心室外拍 (VEB) 情节.
主要方法:
- 预先处理了心电图信号,并提取了RR间隔和QRS复杂参数等特征.
- 使用了四种分类模型,包括支持向量机,k-平均最近邻居,最近平均分类器和最近RMS (NRMS) 分类器.
- 模型在VEB检测的开源和专有ECG数据库上进行了训练和测试.
主要成果:
- 与其他模型相比,最近的RMS (NRMS) 分类器表现出优异的性能.
- 在VEB检测方面,NRMS实现了高精度 (98.68%在开源上,99.97%在专有上) 和F1得分 (94.12%在开源上,94.54%在专有上).
- 在两个数据库中都观察到出色的召回率 (100%和98.62%) 和特异性 (98.53%和99.98%).
结论:
- 拟议的信号处理框架有效地检测长期心电图信号中的心室外拍 (VEB) 情节.
- 开发的自动化系统显示了将其整合到临床实践中,以实现高效的心脏诊断的巨大潜力.
- 该NRMS分类器为自动VEB检测提供了强大而准确的解决方案,减少了医生的工作量.
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