频域深度学习与非侵入性特征用于手术内低血压预测
IEEE journal of biomedical and health informatics
|May 20, 2024
概括
这项研究引入了一种新的深度学习模型,该模型分析频率域中的生物信号,以预测手术内低血压. 频域方法显著提高了预测准确性,特别是使用非侵入性数据.
科学领域:
- 麻醉学和重症监护医学
- 生物医学工程 生物医学工程
- 在医疗保健中的数据科学.
背景情况:
- 术内低血压可能会导致术后器官功能障碍的风险.
- 传统方法依赖于侵入性动脉压力,限制生物信号整合和信号周期性分析.
- 深度学习的进步凸显了频域信息对生物信号分析的有用性.
研究的目的:
- 开发一种深度学习方法,利用频域信息整合多种生物信号模式.
- 解决传统时间域分析在预测手术内低血压方面的局限性.
- 提高深度学习模型的可解释性,用于临床应用.
主要方法:
- 使用离散的里埃转换提取的频率信息,与时间域数据作为深度学习模型的输入相结合.
- 集成可解释的深度学习模块,以提高结果的透明度.
- 利用来自3,226名患者的75,994个数据段进行模型培训和验证.
主要成果:
- 提出的频域深度学习模型的性能优于传统的时间域方法.
- 在非侵入性数据 (0.898对比0.853) 的情况下,实现了接受器操作特征曲线 (AUROC) 下的优越区域性能 (0.898对比0.853).
- 确定1.5-3.0赫兹频段对于预测低血压事件至关重要.
结论:
- 频域分析显示了侵入性和非侵入性生物信号的高性能.
- 开发的框架为预测手术内低血压提供了一个新的视角.
- 这种方法提供了显著的性能改善,特别是在非侵入性监测方面.
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