在CNN-LSTM的超参数优化中使用网格搜索对PPG信号进行血压估计
Nurul Qashri Mahardika T1, Yunendah Nur Fuadah1,2, Da Un Jeong1
1Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Republic of Korea.
Diagnostics (Basel, Switzerland)
|August 12, 2023
概括
研究人员开发了一种CNN-LSTM模型,用于准确的连续非侵入性血压测量 (cNIBP),使用光电脉冲图 (PPG) 信号. 这种强大的系统可以提高用于重症监测的血压估计.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 持续的非侵入性血压测量 (cNIBP) 使用光电血压 (PPG) 对于患者监测至关重要.
- 现有的cNIBP系统需要在准确性和精确性方面提高性能,以实现可靠的健康管理.
研究的目的:
- 提出一个强大的血压估计系统,使用卷积长期短期记忆 (CNN-LSTM) 神经网络.
- 加强从PPG信号中提取有意义的信息,以改善血压监测.
- 通过网格搜索方法来减少超参数优化复杂性.
主要方法:
- 使用了包含PPG和动脉血压 (ABP) 信号的MIMIC III数据集.
- 实现了一个CNN-LSTM模型,其中有五个卷积层,一个LSTM层和两个完全连接的层.
- 采用五倍交叉验证和网格搜索来进行最佳的超参数选择和模型验证.
主要成果:
- 达到标准偏差 (SD) 的7.89 ± 3.79 mmHg 系统血压 (SBP) 估计.
- 达到平均绝对误差 (MAE) 为5.34 ± 2.89 mmHg,用于对腹压血压 (DBP) 的估计.
- 证明了满足BHS,AAMI和IEEE血压监测设备标准的令人满意的性能.
结论:
- 拟议的CNN-LSTM模型为持续的非侵入性血压估计提供了强大而准确的解决方案.
- 网格搜索方法有效优化了CNN-LSTM的超参数,改进了PPG信号分析.
- 该系统符合已确定的国际标准,表明其在血压监测中的临床应用潜力.
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