使用床脚下的载荷细胞,轻松估计血压的可行性
1Sleep Number Labs, San Jose, CA 95113, USA.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
研究人员使用床上传感器数据开发了一个深度神经网络,以重建血压波形. 这种方法显示出对不引人注目的,持续的血压监测和早期心血管疾病检测的希望.
科学领域:
- 生物医学工程 生物医学工程
- 心血管健康 心血管健康
- 人工智能在医学中的应用
背景情况:
- 持续监测血压对于预防心血管疾病至关重要.
- 目前用于血压监测的方法可能是不方便的,不适合连续或睡眠监测.
- 早期发现心血管风险和及时治疗对患者的结果至关重要.
研究的目的:
- 开发一种非侵入性方法,利用床上装载的力传感器的数据重建血压波形.
- 从重建的BP波形来估计静缩血压 (SBP) 和静缩血压 (DBP).
- 探索深度神经网络在连续血液动力学监测中的潜力.
主要方法:
- 利用来自40名参与者的公开数据与同步的负载单元 (力传感器) 和连续的BP波形.
- 采用了三层重复的深度神经网络,使用负载细胞数据作为输入来重建BP波形.
- 将数据分成培训,验证和测试集,确保每个集中的参与者数据独家性.
主要成果:
- 实现了血压波形重建性能,R平方 (R2) 为0.61,平均绝对误差<0.1 mmHg.
- 估计的SBP和DBP平均值显示了Bland-Altman协议极限,分别为[-11.99至15.52毫米]和[-7.95至+3.46毫米].
- 重建的波形使得除了SBP和DBP之外,可以计算额外的血液动力学参数.
结论:
- 深度神经网络模型有效地从床上传感器数据中重建血压波形.
- 该方法显示了检测血压显著变化的潜力,表明心血管健康变化.
- 这种方法为改善心血管护理提供了一条通往不引人注目的,持续的血压监测的途径.
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