将转移学习与血压图分析相结合,用PPG信号来估计血压
Shyamala Subramanian1,2, Sashikala Mishra3, Shruti Patil4
1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India, 412115. shyamalamathi123@gmail.com.
Scientific reports
|November 12, 2025
概括
这项研究开发了一种深度学习模型,用于使用光聚缩学信号持续估计血压. ConvNeXtTiny展示了准确的,非侵入性的血压监测,有助于早期发现心血管疾病.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心血管健康监测 监测心血管健康
背景情况:
- 持续的血压 (BP) 监测对于心血管健康评估和早期发现心脏疾病至关重要.
- 血压升高是潜在心脏并发症的关键指标,需要可靠的监测方法.
研究的目的:
- 开发和验证一种基于深度学习的方法,用于使用光聚血学 (PPG) 信号准确估计血压.
- 探索各种深度学习模型的有效性,与转移学习集成,用于非侵入性血压测量.
主要方法:
- 用连续波形变换 (CWT) 将光电晶体图 (PPG) 信号转换为晶体图.
- 六个深度学习模型 (VGG16,ResNet50,InceptionV3,NASNetLarge,InceptionResNetV2,ConvNeXtiny) 被用来进行深度特征提取.
- 提取的特征与随机森林回归模型用于BP估计.
- 使用平均绝对误差 (MAE) 和标准偏差 (SD) 评估性能,并遵守AAMI和BHS临床标准.
主要成果:
- 在BP估计中,ConvNeXtTiny和VGG16模型表现出强的表现.
- ConvNeXtTiny在缩血压下实现了MAE为2.95 mmHg和SD为4.11 mmHg,而在缩血压下实现了MAE为1.66 mmHg和SD为2.60 mmHg.
- 结果与已建立的临床标准 (AAMI,BHS) 相一致,表明其可靠性很高.
结论:
- 深度学习和转移学习使PPG信号可靠地估计血压.
- ConvNeXtTiny模型提供了一种可靠的,非侵入性的方法,用于持续监测血压,满足临床要求.
- 这种方法可以显著提高心血管健康监测,并促进早期发现心脏问题.
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