对呼吸速率和不确定性估计的不平衡功率光谱生成基于光电显微信号的估计
Soojeong Lee1, Mugahed A Al-Antari2, Gyanendra Prasad Joshi3
1Department of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
本研究引入了一种使用引导生成数据的新方法,以提高机器学习准确度,从家庭健康监测中估计呼吸率,解决数据不平衡,以更好地检测疾病.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
- 生物信号的信号处理.
背景情况:
- 准确的呼吸速率 (RR) 估计对于老年人心肺功能监测至关重要.
- 健康监测系统中的机器学习 (ML) 算法因生物信号数据不平衡而容易出现错误.
- 在正常呼吸,呼吸不良和呼吸不良数据之间的显著样本大小差异会对ML模型的训练和准确性产生负面影响.
研究的目的:
- 提出一种新的方法,将基于引导的不平衡连续功率光谱生成 (IPSG) 与ML相结合,以准确估计RR和相关的不确定性.
- 解决家庭健康监测系统中生物信号数据不平衡的挑战.
- 提高ML驱动的医疗决策对呼吸系统疾病的可靠性.
主要方法:
- 开发了一种不平衡的连续功率光谱生成 (IPSG) 技术,采用非参数式引导式方法.
- 创建人工特征曲线以增强呼吸信号中的稀疏数据段 (呼吸不良,低呼吸).
- 整合原始和人工特征曲线,在组合数据集上训练机器学习模型,如高斯过程回归 (GPR).
主要成果:
- IPSG方法有效地解决了数据不平衡问题,使得能够有效地进行训练,预测光电聚光学信号特征和RR之间的关系.
- 与标准方法相比,拟议的GPR-IPSG模型显示出更好的预测准确性和不确定性量化.
- 在Beth Israel Deaconess医疗中心数据集上,RR估计的平均绝对误差为每分钟0.79和1.47次呼吸 (brpm).
结论:
- GPR-IPSG模型为准确的RR估计提供了强大而稳定的框架,显著增强了基于家庭的健康监测系统.
- 该方法成功地减轻了用于呼吸监测的生物信号分析数据不平衡的影响.
- 这种方法为设计先进的临床家庭监测解决方案提供了可靠的基础.
关键词:
启动链条 (bootstrap) 是一个启动链条.摄影复合声学 (photoplethysmography) 是一种摄影仪.功率光谱曲线的功率曲线呼吸速率估计 呼吸速率估计不确定性是一种不确定性.更多相关视频
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