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在咳声音中识别独特的光谱指纹,用于诊断呼吸道疾病
Syrine Ghrabli1,2, Mohamed Elgendi3, Carlo Menon4
1Biomedical and Mobile Health Technology Lab, ETH Zurich, 8008, Zurich, Switzerland.
Scientific reports
|January 5, 2024
概括
使用智能手机分析咳声音提供了一种具有成本效益的方法来诊断COVID-19等呼吸道疾病. 咳中的特定声学特征可以区分疾病,为可访问的诊断铺平了道路.
科学领域:
- 生物医学工程 生物医学工程
- 呼吸系统医学 呼吸系统医学
- 数据科学数据科学数据科学
背景情况:
- 咳是各种呼吸道疾病的常见症状,包括COVID-19.
- 传统的诊断方法往往是昂贵的,需要专门的专业知识.
- 咳声音的声学分析为可访问,低成本的疾病检测提供了潜力.
研究的目的:
- 研究使用咳声音信号来区分COVID-19,肺炎和喘的潜力.
- 为了识别与不同呼吸道疾病相关的咳声音中的特定声学特征.
- 评估机器学习算法用于基于咳的呼吸道疾病分类的可行性.
主要方法:
- 收集和分析了来自1183名COVID-19患者和341名非COVID-19患者的咳声音.
- 比较了肺炎和喘患者的咳声音.
- 优化的频率范围和应用的统计分离性测试和机器学习分类器 (LDA,k-NN).
主要成果:
- 在咳声音中确定了与COVID-19,肺炎和喘相关的特定频段.
- 统计测试证实了这些声学特征的分离性.
- 机器学习模型成功地证实了与特定呼吸道疾病相关的不同频段.
结论:
- 咳声音分析揭示了不同呼吸系统疾病的独特声学特征.
- 这种方法为疾病分类和诊断提供了一种有希望,具有成本效益和可访问的方法.
- 进一步发展可能会导致基于智能手机的呼吸系统健康监测的广泛使用.
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