从使用机器学习的睡眠充血镜估计夜间血压
Gizem Yilmaz1, Xingyu Lyu1,2, Ju Lynn Ong1
1Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
机器学习可以准确地预测夜间血压 (BP) 使用指尖光斑血学 (PPG) 在健康的成年人. 这种无袖子的方法提供了一种方便的,非侵入性的方法来监测睡眠期间的血压.
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
- 医疗保健中的机器学习
背景情况:
- 夜间血压升高 (BP) 是心血管疾病 (CVD) 和死亡率的重要危险因素.
- 传统的基于袖子的血压监测对于睡眠评估是不切实际的.
- 使用机器学习的无袖血压监测提供了一个有希望的替代方案.
研究的目的:
- 开发和评估用于预测夜间血压的机器学习算法.
- 使用单通道指尖光电显微镜 (PPG) 在睡眠期间无袖式血压监测.
- 评估这种方法在健康成年人中的可行性.
主要方法:
- 68名健康的成年人接受了夜间的多睡眠图 (PSG),指尖PPG和门诊血压监测 (ABPM).
- 脉冲形态特征从PPG波形中提取出来.
- 采用随机森林模型来预测夜间心缩压 (SBP) 和心缩压 (DBP) 血压.
主要成果:
- 确定最佳预测窗口长度为7秒.
- 该模型实现了SBP的平均绝对误差 (MAE) 为5.72 mmHg,而DBP的平均误差为4.52 mmHg.
- 在预测和测量的BP之间观察到高相关系数 (0.87对于SBP,0.86对于DBP).
- 刚度指数被确定为BP预测中最重要的PPG特征.
结论:
- 基于机器学习的夜间血压预测使用指尖PPG在健康成年人中是可行的.
- 开发的无袖手法准确地捕捉了睡眠期间PPG和BP之间的复杂关系.
- 这项技术为持续的血压监测提供了一种可扩展,方便,经济和非侵入性的方法.
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