通过PPG和个性化深度学习预测孕妇的血压
IEEE journal of biomedical and health informatics
|April 10, 2024
概括
这项研究引入了一种使用光斑血学 (PPG) 进行连续无袖血压监测的3阶段深度学习模型,用于怀孕妇女,提供早期妊娠前的检测和个性化血压预测.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 孕产妇和胎儿医学 孕产妇和胎儿医学
背景情况:
- 孕前带来了怀孕期间的重大风险,需要经常监测血压.
- 传统的血压测量方法往往具有侵入性,并且不便于持续监测.
- 摄影致血镜 (PPG) 为频繁的血压评估提供了一个潜在的非侵入性替代方案.
研究的目的:
- 开发和验证一个深度学习模型,以使用孕妇的PPG数据准确,无袖血压预测.
- 通过持续的血压监测,使得预先怀孕症的早期检测.
- 实现个性化血压监测,以改善孕产妇健康结果.
主要方法:
- 从194名受试者的手腕动脉中收集PPG数据 (154名正常,40名孕妇).
- 开发了三阶段的深度学习方法:基线模型,微调1D-CNN,CBAM,GRU和注意力,然后进行转移学习以实现个性化.
- 使用灵活的传感器补丁用于非侵入性PPG信号采集.
主要成果:
- 微调模型实现了SBP的平均误差为-1.40 ± 7.15 mmHg,DBP的平均误差为-0.44 ± 5.06 mmHg.
- 个性化进一步提高了准确度,达到SBP的 -0.17 ± 1.45 mmHg和DBP的0.27 ± 0.64 mmHg.
- 通过三阶段建模过程显著降低了平均误差.
结论:
- 拟议的三阶段深度学习模型为孕妇的持续,非侵入性血压监测提供了有希望的解决方案.
- 这种方法促进了精确的,个性化的血压跟踪,这对于产前的早期预警和管理至关重要.
- 基于PPG的无袖血压监测具有改善产前护理和降低母亲风险的潜力.
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