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相关概念视频

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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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一个两级整体机器学习框架用于OSA分类,而从噪音的气管呼吸声音醒来时.

Vahid Bastani Najafabadi1, Walid Ashraf2, Ahmed Elwali3

  • 1Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.

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PubMed
概括
此摘要是机器生成的。

使用气管呼吸声 (TBS) 进行阻塞性睡眠呼吸暂停 (OSA) 查,即使在杂的环境中使用廉价麦克风也是可行的. 在清醒状态下对OSA的声学检测显示出更广泛的可访问性.

关键词:
装袋的决策树,可以做出决定.生物医学信号处理整体机器学习 整体机器学习阻塞性睡眠呼吸暂停 (OSA) 是一种基于概率的投票方式.气管呼吸的声音清醒的查 清醒的查波形数据包的分解

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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
  • 机器学习 机器学习

背景情况:

  • 由于复杂的睡眠研究,阻塞性睡眠呼吸暂停 (OSA) 的诊断不足.
  • 在清醒状态下进行气管呼吸声音 (TBS) 分析是潜在的查替代方案.
  • 噪音环境和低成本设备对声学OSA检测构成挑战.

研究的目的:

  • 评估使用在噪音条件下用廉价麦克风录制的TBS检测OSA的可行性.
  • 开发和评估基于清醒气管声音的OSA查的机器学习算法.

主要方法:

  • 对247名不同程度的OSA患者的TBS分析.
  • 呼吸声音的细分,声学特征提取和维度缩小.
  • 实施一个双层组合分类器,使用袋式决策树和概率投票.

主要成果:

  • 该算法实现了77.1%的准确性,84.3%的灵敏度和59.9%的特异性.
  • 性能低于使用高质量的设备在安静环境中的研究.
  • 麦克风质量显著影响了分类性能.

结论:

  • 在清醒期间对OSA进行声学选,即使在具有挑战性的杂环境中也是可行的.
  • 可以使用廉价的麦克风,但性能对设备质量敏感.
  • 需要进一步的研究来优化算法,以适应现实世界和噪音条件.