使用机器学习算法从最佳PPG特征特征进行连续无袖血压测量,使用机器学习算法
Araf Nishan1, S M Taslim Uddin Raju1, Md Imran Hossain1
1Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna - 9203, Bangladesh.
Heliyon
|March 27, 2024
概括
这项研究引入了一种新的,无袖子的方法,用于非侵入性血压 (BP) 估计,使用光聚血图 (PPG) 信号. 开发的技术准确地估计了心压,心压和平均动脉压,符合高临床标准.
科学领域:
- 生物医学工程 生物医学工程
- 心血管健康 心血管健康
- 信号处理 信号处理
背景情况:
- 高血压检测依赖于血压 (BP) 测量,这对于预防和治疗心血管疾病至关重要.
- 传统的基于袖子的血压监测是不舒服的,并且限制了连续测量.
- 非侵入性,无袖的血压监测对于改善患者护理是非常可取的.
研究的目的:
- 提出和验证一种新的非侵入性,无袖子的方法来估计静脉血压 (SBP),平均动脉压 (MAP) 和心动脉血压 (DBP).
- 为了利用光聚体图 (PPG) 信号和非线性回归算法的特征特征,以准确估计BP.
- 使用选定的特征来比较BP估计的各种回归模型的性能.
主要方法:
- 从219名参与者收集了PPG信号.
- 从PPG及其衍生品中提取了46个时间,频率和时间频率域特征,包括年龄和性别.
- 应用基于关联的特征选择 (CFS) 和缓解F (ReliefF) 用于特征选择.
- 开发了使用支持向量回归 (SVR),K-最近邻居回归 (KNR),决策树回归 (DTR) 和随机森林回归 (RFR) 的BP估计模型.
主要成果:
- 使用ReliefF功能选择的SVR模型实现了最佳性能,SBP的平均绝对误差为2.49 mmHg,MAP的平均误差为1.62 mmHg,DBP的平均误差为1.43 mmHg.
- 拟议的方法符合医学仪器仪表的进步标准,用于BP估计.
- 根据英国高血压协会标准,SBP,MAP和DBP的A级结果.
结论:
- 开发的方法允许仅从PPG信号特征进行非侵入性血压估计.
- 不需要手套或校准,比传统方法提供了显著的优势.
- 这种方法有望实现连续和舒适的BP监测.
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