睡眠BP-Net:一个时间分布的卷积网络,用于从光电脉冲图 (Photoplethysmogram) 来估计夜间血压
Boshra Khajehpiri1, Eric Granger1, Massimiliano de Zambotti2
1École de technologie supérieure (ÉTS), Université du Québec, Montréal, QC H3C 1K3, Canada.
概括
这项研究介绍了SleepBP-Net,这是一种新的AI模型,用于使用夜间光多血学 (PPG) 信号进行无袖,非侵入性夜间血压 (BP) 估计. 该模型有效地捕捉长期睡眠模式,以准确监测血压.
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
- 医疗保健中的人工智能
背景情况:
- 夜间血压 (BP) 监测对于评估心血管健康至关重要.
- 目前用于一夜间血压测量的方法通常是侵入性的或繁的.
- 需要非侵入性,无袖技术来持续监测睡眠BP.
研究的目的:
- 为了研究从夜间光电血压 (PPG) 信号中估计夜间血压.
- 开发和验证用于长期夜间BP估计的新型机器学习模型.
- 评估轻量级卷积循环网络的有效性,以便在睡眠期间轻松地监测血压.
主要方法:
- 开发SleepBP-Net,一个时间分布的卷积循环网络,用于夜间BP估计.
- 在临床睡眠实验室环境中收集的使用过夜PPG信号.
- 使用Portapres BP测量作为参考验证模型,采用离开一个主体的交叉验证.
主要成果:
- 最初的模型在1分钟的PPG序列中实现了15.7 mmHg (SBP) 和12.1 mmHg (DBP) 的平均绝对误差 (MAE).
- 个性化的模型显著提高了准确度,达到7.8 mmHg (SBP) 和5.9 mmHg (DBP).
- 将序列长度延长到30分钟进一步将MAE降低到7.2 mmHg (SBP) 和5.7 mmHg (DBP),证明了长期模式的价值.
结论:
- 睡眠BP-Net通过利用长期时间模式,有效地从PPG信号中估计夜间BP.
- 混合卷积循环网络在这个任务中表现出比纯卷积方法更高的性能.
- 拟议的模型提供了一个有前途的解决方案,用于不引人注目的,高效和准确的真实世界夜间BP监测.
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