基于机器学习的咳音调分类:慢性阻塞性肺病和呼吸道感染的诊断探索
Xiaohan Liu1, Tengteng Li2, Jingxin Zhang1
1Beijing University of Chinese Medicine.
Journal of visualized experiments : JoVE
|October 6, 2025
概括
这项研究表明,语音分析和机器学习可以区分慢性阻塞性肺病 (COPD) 和呼吸道感染 (RTI). 后勤回归模型实现了高精度,提供了一个非侵入性诊断工具.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 肺部病理学 肺部病理学
背景情况:
- 区分慢性阻塞性肺病 (COPD) 和呼吸道感染 (RTI) 对于适当的患者管理至关重要.
- 目前的诊断方法可能是侵入性的或耗时的.
- 不侵入性,客观的工具是需要差异诊断.
研究的目的:
- 开发和验证一种使用语音信号分析来区分COPD与RTI的非侵入性方法.
- 将机器学习模型应用于语音特征进行分类.
- 评估物流回归 (LR) 和随机森林 (RF) 模型的性能.
主要方法:
- 收集了25名COPD患者和25名RTI患者的固定模式语音信号.
- 分析了400多个多维语音特征以确定显著差异.
- 选择了18个统计学上显著的特征 (P < 0.05) 并应用了缩小维度.
- 经过训练和评估的LR和RF模型进行分类.
主要成果:
- 18个声音特征显著区分了COPD与RTI患者.
- 后勤回归 (LR) 模型实现了曲线下的面积 (AUC) 为0.95.
- 随机森林 (RF) 模型实现了0.76的AUC,表现明显低于LR.
结论:
- 语音分析与机器学习相结合,特别是LR模型,是一种可行且有前途的非侵入性工具,用于区分COPD与RTI.
- 这种方法为呼吸系统疾病的差异诊断提供了临床应用的基础.
- 这种基于语音的诊断方法需要进一步优化.
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