HGCTNet:手工制作的特征引导CNN和变压器网络,用于可穿戴的无袖手血压测量仪
IEEE journal of biomedical and health informatics
|April 30, 2024
概括
通过结合CNN和变压器,HGCTNet通过使用可穿戴设备改善无袖血压 (BP) 估计. 这种新的方法减少了系统血压和透气血压的估计错误,提高了血压监测的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习用于医疗保健
背景情况:
- 像ECG和PPG这样的可穿戴生物信号含有冗余信息和复杂的时间依赖.
- 从这些信号中提取无袖血压 (BP) 估计的准确特征仍然是一个重大挑战.
- 现有的方法难以有效地在生物信号中处理本地和全球时间信息.
研究的目的:
- 开发一个先进的深度学习模型,HGCTNet,使用可穿戴设备数据进行准确的无袖手套血压估计.
- 利用混合CNN-变压器架构来捕获生物信号的综合特征.
- 整合手工制作的功能和人口统计数据,以进一步提高BP估计性能.
主要方法:
- 提出了HGCTNet,这是一个混合CNN-变压器网络,集成卷积操作和自我注意机制.
- 实施了一种手工制作的特征引导注意模块,以减少使用手工制作的信号特征来学习特征的冗余性.
- 采用了功能融合模块,将学习的功能,手工制作的功能和人口统计数据结合起来,用于最终的BP预测.
主要成果:
- 在CAS-BP数据集上,HGCTNet实现了较低的估计误差:0.9 ± 6.5 mmHg (DBP) 和0.7 ± 8.3 mmHg (SBP).
- 在Aurora-BP数据集中,误差为-0.4 ± 7.0 mmHg (DBP) 和-0.4 ± 8.6 mmHg (SBP).
- 与最先进的方法相比,显著减少了SBP平均绝对误差10.68% (CAS-BP) 和9.84% (Aurora-BP).
结论:
- HGCTNet有效地从可穿戴生物信号中提取分辨特征,用于无袖式BP估计.
- 混合架构和功能融合战略提高了BP监控的准确性并减少了冗余性.
- 与现有方法相比显示了显著的改进,突出了HGCTNet在先进可穿戴血压测量方面的潜力.
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