基于近红外光谱信号的三种麻醉阶段的分类
IEEE journal of biomedical and health informatics
|June 4, 2024
概括
近红外光谱 (NIRS) 和机器学习有效地分类麻醉阶段. 在NIRS信号中,相振幅合显示了更深度麻醉的增加活性,有助于外科安全.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 麻醉学 麻醉学
背景情况:
- 麻醉阶段的监测对于手术安全至关重要.
- 当前的监测方法可能有局限性.
- 开发先进的,非侵入性的监测技术是必不可少的.
研究的目的:
- 使用近红外光谱学 (NIRS) 信号和机器学习来分类不同的麻醉阶段.
- 调查NIRS衍生脑血动力学变量与麻醉深度之间的关系.
- 评估NIRS用于实时麻醉监测的可行性.
主要方法:
- 收集的NIRS信号,特别是保持 (MNT),出现 (EM) 和意识 (CON) 麻醉阶段的右近位氧血球蛋白 (HbO2).
- 分析了相振幅合 (PAC) 来比较阶段之间的差异.
- 提取的时间域 (线性和非线性) 和频域特征.
- 使用支向量机 (SVM) 分类器进行三阶段分类.
主要成果:
- NIRS信号的相幅度合 (PAC) 显示,随着麻醉深度的增加,逐渐增强.
- 实现了69.27%的三种分类准确度,超过了单一特征分类.
- 组合的功能集提高了分类性能.
结论:
- 使用机器学习分析的NIRS信号显示了分类麻醉阶段的可行性.
- PAC是麻醉深度的一个有希望的指标.
- 这种方法为开发新型麻醉监测系统提供了潜力,以提高患者的安全性.
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