基于PPG的双路径无袖血压估计,使用调节器和旋转器变压器.
IEEE journal of biomedical and health informatics
|December 8, 2025
概括
这项研究提出了新的深度学习模型,即Conformer-Transformer和1D Swin Transformer,用于使用光电图 (PPG) 信号进行准确的无袖式血压监测. 这些先进的方法显著改善了对静缩和静缩血压的估计.
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
- 心血管技术的心血管技术
背景情况:
- 持续的,非侵入性血压 (BP) 监测仍然是医疗保健中的一个重大挑战.
- 现有的方法通常依赖于侵入性程序或不太准确的估计.
- 光电缩图 (PPG) 信号是无袖血压监测的有希望的途径,因为它们的可访问性.
研究的目的:
- 引入一种新的双路径深度学习框架,用于持续的,非侵入性的无袖血压监测.
- 适应和应用两种新型深度神经网络架构,即Conformer-Transformer和1D Swin Transformer,用于PPG信号分析.
- 为了提高系统血压 (SBP) 和透气血压 (DBP) 估计的准确性和生理一致性.
主要方法:
- 开发使用PPG信号的双路径深度学习框架.
- 构造器-变压器和1D Swin变压器架构的实施和调整.
- 模拟PPG信号的形态结构和节奏动态,用于动脉血压 (ABP) 波形重建.
- 在两个公共数据集上进行了广泛的实验验证.
主要成果:
- 调节器-变压器在SBP (2.979 mmHg) 和DBP (1.603 mmHg) 中实现了最低的平均绝对误差 (MAE),超过了之前的研究.
- 1D Swin 变压器表现出强的性能,其缩MAE 为3.034 mmHg,腹缩MAE为1.714 mmHg.
- 这两种模型显著提高了BP估计的准确性和生理一致性,符合BHS级A和AAMI标准.
- 实现了高动脉血压 (ABP) 波形重建.
结论:
- 拟议的双路径深度学习框架,包括Conformer-Transformer和1D Swin Transformer,在无袖血压监测方面取得了重大进展.
- 这些新型架构有效地模拟PPG信号特征,以准确可靠的SBP和DBP估计.
- 这项研究表明了先进的深度学习技术在非侵入性心血管监测方面的潜力,符合严格的临床标准.
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