一个双分支的ResNet-BiLSTM深度学习框架,用于提取应用到PPG无袖血压估计的多式特征
Zenan Liu1, Minghong Qiao1, Yezi Liu1
1College of Biomedical Engineering, Sichuan University, Chengdu 610065, China.
这项研究引入了一种新的深度学习模型,用于使用光聚光学 (PPG) 信号无袖式血压监测. 创新的ResNet-BiLSTM框架准确估计血压,为传统方法提供了方便的替代方案.
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 心血管健康
背景情况:
- 心血管疾病对健康构成重大风险,血压水平是关键指标.
- 持续监测血压至关重要,但受到传统袖口式设备的不便所阻碍.
- 现有的深度学习方法用于无袖血压估计,往往缺乏解释性,限制了准确性.
研究的目的:
- 开发一种新的,准确的,可解释的深度学习框架,用于使用光聚缩学 (PPG) 进行无袖血压估计.
- 结合剩余网络 (ResNet) 和双向长期短期存储器 (BiLSTM) 的优势,进行增强的PPG信号分析.
- 根据已建立的临床标准验证拟议的方法.
主要方法:
- 设计了一个集ResNet和BiLSTM的双分支深度学习架构.
- ResNet分支处理了通过支持矢量机-递归特征消除 (SVM-RFE) 选择的60个特征,包括新的趋势特征.
- 该BiLSTM分支机构分析了完整的PPG波形.
- 该模型在MIMIC-IV数据集上进行了训练和测试,该数据集包括218名患者的220个波形段.
主要成果:
- 拟议的ResNet-BiLSTM模型实现了缩血压的平均绝对误差为3.47 mmHg,缩血压的平均误差为2.81 mmHg.
- 标准偏差为5.06 mmHg (心缩) 和4.11 mmHg (腹缩).
- 性能符合医学仪器仪表协会 (AAMI) 的标准,并获得英国高血压协会 (BHS) 标准的"A"评级.
结论:
- 这种新的双分支深度学习框架为准确和方便的无袖血压估计提供了一个有前途的解决方案.
- ResNet和BiLSTM的组合有效地利用了提取的特征和原始PPG波形.
- 取得的性能验证了这种基于PPG的先进监测技术的临床适用性和潜力.
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