癌症患者的生命体征监测基于双路径传感器和分频-CNN模型
IEEE journal of biomedical and health informatics
|February 21, 2024
概括
一种新型的双路径光纤传感器和分频-CNN模型准确地监测癌症患者的心率 (HR) 和呼吸率 (RR),提高稳定性并减少与传统方法相比的错误.
科学领域:
- 生物医学工程 生物医学工程
- 医疗监测 医疗监测
- 传感器技术 传感器技术
背景情况:
- 对于癌症患者来说,传统的生命体征监测面临着深度学习的不稳定性和传感器特定挑战.
- 精确的心率 (HR) 和呼吸率 (RR) 监测对于癌症患者的护理至关重要.
研究的目的:
- 开发一个强大的双路径微曲线光纤传感器和一个有针对性的分频-CNN (DFC) 模型,以精确测量癌症患者的HR和RR.
- 提高生命体信号监测的抗干扰能力和临床相关性.
主要方法:
- 一个双路径微曲线光纤传感器,利用频率划分和稳定相提取来最大限度地减少运动工件干扰.
- 一个设计用于从传感器数据中强大的功能学习的DFC模型.
- 从双路径估计HR和RR的加权策略,以减轻单源错误.
主要成果:
- 提出的方法在错误,相关性和与癌症患者医院设备的一致性方面表现良好.
- 实现了较低的错误率:每分钟3.51次 (有疼痛) 和每分钟2.53次 (没有疼痛).
- 与之前的工作相比,在两组患者中显著改善了报告时间间隔和降低了平均值/信心间隔.
结论:
- 双通道光纤传感器与DFC模型相结合,为癌症患者的HR和RR监测提供了稳定而准确的解决方案.
- 这种方法提高了抗干扰能力和临床适用性,优于以前的方法.
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