通过使用多个嵌入式的深度学习方法解开血压估计
Vishal Singh Roha1, Mehmet R Yuce1
1Department of Electrical and Computer Systems, Monash University, Wellington Rd, Clayton, Melbourne, 3800, Victoria, Australia.
Computers in biology and medicine
|December 9, 2025
概括
这项研究提出了一种新的机器学习方法,用于估计血压 (BP) 使用脉冲到达时间 (PAT) 没有校准. 该框架证明了连续BP监测的高精度和临床适用性.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 心血管监测 心血管监测
背景情况:
- 精确的血压 (BP) 监测对于心血管健康管理至关重要.
- 现有的血压测量方法往往需要侵入性程序或频繁的校准.
- 持续的,非侵入性的血压监测仍然是一个重大的临床挑战.
研究的目的:
- 开发和验证一种无校准的机器学习框架,用于使用脉冲到达时间 (PAT) 估计缩血压 (SBP) 和腹缩血压 (DBP).
- 通过先进的功能工程和深度学习技术,提高BP估计的准确性和可靠性.
- 评估拟议框架在不同数据集中的临床适用性和通用性.
主要方法:
- 使用的脉冲到达时间 (PAT) 来自心电图 (ECG) 和光电脉冲图 (PPG) 信号.
- 整合了基于相似性的功能,使用欧几里德和曼哈顿距离矩阵来增强模式识别.
- 采用注意引导卷积神经网络 (CNN) 进行BP预测.
- 在三个独立数据集上验证了框架:卡布里尼医院,PTT PPG和MIMIC-II.
主要成果:
- 在数据集中实现了SBP (0.89-0.95) 和DBP (0.89-0.94) 的高R平方值.
- 报告SBP (1.31-6.45 mmHg) 和DBP (0.98-2.92 mmHg) 的低平均绝对误差.
- 符合医疗仪器 (AMI) 标准的进步,并在多个数据集上获得英国高血压学会 (BHS) "A"/"B"等级.
结论:
- 拟议的无校准机器学习框架提供了准确可靠的BP估计.
- 该框架表现出强大的通用性,实时兼容性和临床适用性.
- 这种方法为持续,舒适和非侵入性血压监测提供了有希望的解决方案.
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