在健康监测应用中进行连续血压估计的集成式AI方法
Rafita Haque1, Chunlei Wang2, Nezih Pala1,3
1Electrical and Computer Engineering Department, Florida International University, Miami, FL 33174, USA.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
这项研究引入了一个人工智能框架,用于使用光电血图数据持续估计血压. 这种新的方法实现了高精度,有助于早期发现心血管疾病.
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
- 心血管生理学心血管生理学
背景情况:
- 持续的血压 (BP) 监测对于了解各种生理状态期间的心血管调节至关重要.
- 它有助于早期检测心血管疾病 (CVD),如高血压和中风.
- 目前用于连续血压监测的方法可能具有侵入性或缺乏全面的动态洞察力.
研究的目的:
- 开发和评估一个人工智能驱动的框架,用于非侵入性,持续的血压估计.
- 为了利用光电脉冲图 (PPG) 信号和人口统计数据进行准确的血压预测.
- 推进动态的心血管健康监测解决方案.
主要方法:
- 专家驱动的功能工程来自基于PPG的动脉脉冲波形 (APW),包括脉冲率,波形形态和强度.
- 提取的生理特征与人口统计数据 (年龄,性别,BMI) 的融合.
- 使用Tab-Transformer进行功能嵌入,随后使用了一组CatBoost,XGBoost和LightGBM模型.
主要成果:
- 人工智能框架实现了系统性BP (SBP) 的平均绝对误差 (MAE) 为3.87mmHg,而透气性BP (DBP) 的平均误差为2.50mmHg.
- 性能符合严格的准确性标准,包括英国高血压学会 (BHS) A级和医学仪器仪表协会 (AAMI).
- 该模型在1000名受试者的多样化数据集中展示了强度和准确性.
结论:
- 拟议的人工智能框架为持续的血压监测提供了一个有希望的非侵入性解决方案.
- 这项技术可以显著改善心血管疾病的早期识别和管理.
- 该研究推进了人工智能驱动的工具,用于动态的心血管健康评估和个性化医疗.
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