基于启动带功率光谱曲线的不平衡特征生成用于估计呼吸速率
Soojeong Lee1, Gyanendra Prasad Joshi2, Gangseong Lee3
1Department of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul, 05006, Korea.
Scientific reports
|May 21, 2025
概括
准确的呼吸速率 (RR) 估计对于老年人来说至关重要. 一种新方法使用基于引导的不平衡特征生成来提高RR的机器学习准确性,解决数据不平衡问题.
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
- 心脏呼吸系统监测 监测心脏呼吸系统
背景情况:
- 老年人快速呼吸速率 (RR) 变化表明潜在的严重疾病,需要准确估计心肺呼吸能力评估.
- 机器学习算法与数据不平衡作斗争,其中某些呼吸模式 (正常呼吸) 的样本大小比其他样本大 (呼吸障碍,呼吸障碍),导致重大错误.
- 由于这些数据失衡引起的不准确性,现有的方法往往不适合医疗决策.
研究的目的:
- 引入一种新的方法,将基于引导的不平衡特征生成 (BIFG) 与高斯过程相结合,用于准确的RR估计和不确定性量化.
- 解决呼吸速率监测数据不平衡的关键问题,特别是关于呼吸不良和呼吸不良等代表性不足的模式.
- 提高机器学习模型的可靠性,用于医疗决策心脏呼吸系统健康.
主要方法:
- 开发了一种新的方法,将基于引导的不平衡特征生成 (BIFG) 与高斯过程集成在一起.
- 利用参数启动模型生成人工特征曲线,特别针对代表性不足的数据段 (呼吸不良和呼吸不良).
- 采用非参数引导方法来增加人工特征曲线生成,选择性地将它们应用于不平衡的数据部分,以改进模型训练.
主要成果:
- 拟议的BIFG方法有效地解决了RR估计中的数据不平衡.
- 实现了对光聚光学信号特征和参考RR之间的复杂非线性关系的准确预测.
- 证明了卓越的预测性能和不确定性量化,在两个数据集中平均绝对误差为每分钟0.89和1.44次.
结论:
- BIFG方法显著提高了老年人RR估计的准确性和可靠性,即使使用不平衡的数据集.
- 这种方法为心肺呼吸监测中的机器学习提供了强大的解决方案,提高了它在医疗决策中的实用性.
- 准确估计RR及其相关的不确定性为评估心肺健康和检测严重疾病提供了宝贵的见解.
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