基于机器学习的血压估计,使用阻抗心脏图数据
T L Bothe1, A Patzak2, O S Opatz1
1Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Acta physiologica (Oxford, England)
|January 13, 2025
概括
使用阻抗心图 (ICG) 的机器学习算法显示出估计血压 (BP) 的可行性. 这种非侵入性方法为传统的手式血压测量提供了潜在的替代方案.
科学领域:
- 生物医学工程 生物医学工程
- 心血管生理学心血管生理学
- 机器学习 机器学习
背景情况:
- 准确的血压 (BP) 测量对于心血管疾病管理至关重要.
- 传统的基于袖子的血压测量方法可能不准确且不舒服.
- 阻抗心脏图 (ICG) 为BP估计提供了一个潜在的非侵入性替代方案.
研究的目的:
- 评估机器学习 (ML) 算法的可行性,以使用ICG数据估计BP.
- 为了比较各种ML算法在BP估计中的性能与标准袖口测量.
- 在精神和身体压力条件下评估血压估计准确度.
主要方法:
- 分析了71名健康成年人的ICG数据.
- 评估了9个ML算法来估计BP与振荡计BP测量.
- 优化模型以最大限度地减少根平均平方误差,并使用精度和回归指标评估性能.
- 在特里尔社会压力测试和自行车人体表练习中测试模型.
主要成果:
- 多线性回归实现了缩血压的高精度 (r=0.82,MAE=8.20 mmHg).
- 支持向量的回归显示了对腹痛血压的最高准确度 (r=0.51,MAE=6.05 mmHg).
- 这两种模型都显示了无袖式BP估计的可行性.
结论:
- 使用ICG数据的机器学习算法可用于估计参考血压.
- 需要进一步的研究来解决偏见,提高性能,并标准化临床应用的验证.
- 基于ICG的ML为非侵入性BP监测提供了一个有希望的途径.
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