血压估计的发展:从特征分析到基于图像的深度学习模型
Vishal Singh Roha1, Rahul Ranjan2, Mehmet Rasit Yuce3
1Department of Electrical and Computer Systems, Monash University, Wellington Rd, Clayton, 3800, Melbourne, VIC, Australia. vishal.roha@monash.edu.
Journal of medical systems
|July 9, 2025
概括
这项研究引入了一个新的AI框架,用于无袖式血压 (BP) 估计,仅使用光聚光显微镜 (PPG) 图像. 该方法通过使用先进的人工智能分析PPG信号衍生品来提高准确性和临床可靠性,优于传统技术.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 传统的无袖血压 (BP) 估计依赖于来自不同身体部位的多个生理信号 (例如,心电图,PPG),由于噪音和复杂性而带来挑战.
- 现有的方法,如脉冲传输时间 (PTT) 和脉冲到达时间 (PAT),显示与BP的相关性,但受到多站点信号采集和噪声易感性的限制.
研究的目的:
- 开发一个创新的,人工智能驱动的框架,用于无袖血压估计,仅使用单位光多缩图 (PPG) 信号.
- 通过利用先进的人工智能和计算机视觉技术来提高BP估计准确度和临床可靠性.
主要方法:
- 用PPG信号及其第一 (vPPG) 和第二 (aPPG) 导数的图像用于BP估计.
- 使用ResNet-50从PPG,vPPG和aPPG图像中提取特征,识别BP相关区域.
- 应用了多头交叉注意力 (MHCA) 机制来改进特征并改善跨模式信息交换.
主要成果:
- 拟议的框架在BP估计方面表现优越,与传统的PAT和PTT基于的方法相比,在三个不同的数据集中表现优越.
- 通过遵守医学仪器进步协会 (AAMI) 和英国高血压协会 (BHS) 的严格医疗标准,验证了临床可靠性.
结论:
- 由人工智能驱动的框架提供了非侵入性BP监测的实用和准确的进步,只需要单站点的PPG信号.
- 这种方法克服了传统方法的局限性,为更容易获得和可靠的BP监测解决方案铺平了道路.
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