使用机器学习模型探索各种冷凝器麦克风用于手腕脉冲测量的应用
Chetna Sharma1,2, Neha3, Gurinderjit Singh1
1CSIR-Central Scientific Instruments Organisation, Chandigarh, 160030, India.
Physical and engineering sciences in medicine
|January 13, 2026
概括
与全向型相比,单向冷凝器麦克风在手腕脉冲测量方面提供了更高的准确性. 机器学习模型,特别是Nu-Support Vector Regression,提高了脉冲率预测的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 心血管健康监测 监测心血管健康
- 传感器技术 传感器技术
背景情况:
- 手腕脉冲测量对于评估心血管健康至关重要.
- 现有的传感器 (光学,压力,图像,超声波) 面临环境干扰和系统复杂性等局限性.
- 电容麦克风正在成为脉冲检测的可行替代品.
研究的目的:
- 为了评估不同类型的冷凝器麦克风在手腕脉冲测量的有效性.
- 为了比较通向和单向冷凝器麦克风的准确性.
- 评估机器学习模型在使用麦克风数据预测脉冲率方面的性能.
主要方法:
- 三个冷凝器麦克风 (一个全向,两个单向) 被测试.
- 开发了一种机械脉冲采集系统.
- 分析了27名健康受试者的数据,使用四种回归模型 (部分最小平方回归,回归,主要组件回归,Nu-Support Vector回归).
主要成果:
- 单向冷凝器麦克风表现出比全向型更高的准确性.
- 一个单向麦克风的灵敏度为-50~44dB,其性能优于 -40~34dB的麦克风.
- 努-支持向量回归实现了最低的预测误差,显示出卓越的性能.
结论:
- 单向冷凝器麦克风更适合用于手腕脉冲测量.
- 麦克风的灵敏度在准确性方面起着至关重要的作用.
- 机器学习,特别是Nu-Support Vector Regression,显著提高了脉冲率预测的准确性,指导了未来的传感器开发.
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