评估COPD患者的临床和功能状态,使用在恶化期间和之后的语音分析
Wolfgang Mayr1, Andreas Triantafyllopoulos2,3, Anton Batliner2,3
1Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany.
International journal of chronic obstructive pulmonary disease
|January 27, 2025
概括
语音分析准确地分类慢性阻塞性肺病 (COPD) 严重程度,优于传统的分数. 这项技术为远程COPD监测和改善患者管理提供了潜力.
科学领域:
- 肺部医学 肺部医学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 慢性阻塞性肺病 (COPD) 显著影响呼吸功能,言语和咳.
- 准确的COPD严重程度分类对于有效的患者管理和治疗至关重要.
研究的目的:
- 评估基于机器学习的语音分析在分类COPD疾病严重性的有效性.
- 将语音分析的性能与COPD评估的既定临床分数 (BORG,CAT) 进行比较.
主要方法:
- 在单中心研究中招募50名COPD患者 (GOLD II-IV,E组).
- 提取光谱,表声和时空语音特征.
- 使用支持矢量机 (SVM) 模型进行分类,将语音特征与BORG和CAT分数进行比较.
主要成果:
- 语音分析在区分COPD恶化状态时达到84%的准确性,优于BORG和CAT分数.
- CAT分数与阅读节奏相关;BORG尺度与关节稳定性相关.
- 肺功能测试 (PFT) 显示了与语音暂停率和节律变化的相关性.
结论:
- 语音分析显示了作为一种可行的工具来分类COPD状态的潜力.
- 这项技术为COPD患者的远程监测开辟了道路.
- 应用于语音的机器学习为COPD评估提供了一种新的方法.
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