一个用于估计动脉血压的电脉冲人工物信号:一项概念验证研究
Ali Howidi1, Ryan G L Koh1,2, Niveetha Wijendran3
1Institute of Biomedical Engineering, University of Toronto, Toronto, Canada.
Physiological measurement
|December 9, 2025
概括
研究人员开发了一种新型的可植入电极系统,使用电动血管图 (EVG) 和机器学习模型持续预测动脉血压 (BP),在老鼠中显示出高精度.
科学领域:
- 生物医学工程 生物医学工程
- 心血管研究研究心血管研究
- 机器学习在医学中的应用
背景情况:
- 高血压是全球主要的健康问题,需要改善患者监测.
- 持续测量动脉血压 (BP) 可以提高高血压的治疗效果.
- 慢性BP监测面临着重大的技术挑战.
研究的目的:
- 通过使用可植入电极,研究一种用于预测动脉血压的新方法.
- 评估为BP估计生成文物信号的可行性.
- 评估用于连续BP预测的机器学习模型.
主要方法:
- 一个可植入的多接触袖口电极被用来获取大鼠的电动血管电图 (EVG) 信号.
- 处理了EVG信号,提取了像Catch22这样的功能.
- 线性回归,随机森林 (RF) 和卷积神经网络模型预测了缩和腹缩血压.
主要成果:
- 带有Catch22特征的射频模型在预测BP方面表现出很高的性能.
- 预测的血压值在测试组的82.6%-100% (心缩) 和84.1%-99.9% (腹缩) 中的误差小于5mmHg.
- 五次交叉验证证实了强大的性能,准确预测了91.5% (心缩) 和92.4% (腹缩) 的数据.
结论:
- 这项概念验证研究表明了可植入电极和ML用于连续动脉血压监测的潜力.
- 开发的系统显示了未来临床翻译的可行性.
- 临床应用需要进一步的研究和系统开发.
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