动脉网:通过可穿戴的脉冲信号重建动脉血压波形,一种群体意识的方法
Sicong Huang1, Roozbeh Jafari2,3,4,5, Bobak J Mortazavi1
1Department of Computer Science and EngineeringTexas A&M University College Station TX 77840 USA.
IEEE open journal of engineering in medicine and biology
|January 8, 2026
概括
动脉网准确地重建连续动脉血压 (ABP) 波形,非侵入性. 这种人工智能模型提高了估计心压和腹压血压 (SBP/DBP) 的准确性,显示了远程健康监测的前景.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心血管监测 心血管监测
背景情况:
- 持续的动脉血压 (ABP) 监测对于血液动力学评估至关重要,但需要进行侵入性手术.
- 现有的非侵入性方法用于从脉动信号进行ABP重建,通常会产生不准确的静缩和静缩血压 (SBP/DBP) 估计,并且对个体变化敏感.
研究的目的:
- 开发一种新的深度学习模型ArterialNet,用于准确的非侵入性重建连续动脉血压 (ABP) 波形.
- 与现有的非侵入性技术相比,提高SBP/DBP的估计,减少对象的变化.
- 评估ArterialNet在临床和远程医疗环境中的性能和稳定性.
主要方法:
- 动脉网集成了通用的脉动到ABP信号转换与个性化的特征提取.
- 该模型采用混合损失函数和规范化技术来优化性能.
- 模型架构和稳定性通过对数据质量和可用性的废弃研究进行了评估.
主要成果:
- 在MIMIC-III数据集上,ArterialNet实现了5.41 ± 1.35 mmHg的根平均平方误差 (RMSE),在标准偏差方面超过现有信号转换技术的58%.
- 在一个远程健康场景中,ArterialNet重建了ABP波形,RMSE为7.99 ± 1.91 mmHg.
- 废弃性研究证实了单个组件的贡献和模型的稳定性.
结论:
- 动脉网在ABP重建和SBP/DBP估计方面表现出卓越的性能,对象差异显著减少.
- 该模型显示,在偏远的卫生机构中,可靠的血液动力学监测具有相当大的潜力.
- ArterialNet提供了一种强大而准确的非侵入性解决方案,用于持续监测血压.
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