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使用机器学习的自动骨感应听力测量仪的性能和可靠性评估.

Nicolas Wallaert1,2, Antoine Perry3, Hadrien Jean2

  • 1Department of Otorhinolaryngology-Head and Neck Surgery, Rennes University Hospital, Rennes, France.

Trends in hearing
|November 3, 2024
PubMed
概括
此摘要是机器生成的。

一种新的机器学习 (ML) 方法使用额头位置自动化骨传导 (BC) 听力测量,提供更快,更可靠的听力测试. 这种ML-听力测量显示了与成人传统方法相比的性能.

关键词:
听力测量听力测量仪自动化测试自动化测试贝叶斯式学习是贝叶斯式学习.反侧面的掩盖方式心理声学是一种心理声学.这是一个门值.

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科学领域:

  • 听力学 听力学是指听力学.
  • 生物医学工程 生物医学工程
  • 机器学习 机器学习

背景情况:

  • 纯色调听力测量是标准的,但耗时,并提供离散的听力敏度估计.
  • 目前的骨传导 (BC) 听力测量的局限性包括时间承诺和离散数据点.

研究的目的:

  • 开发一种基于机器学习 (ML) 的方法,用于完全自动化的BC听力测量,并放置额头振动器.
  • 为了解决传统听力测试的耗时性质和离散估计.

主要方法:

  • 开发了一种基于ML的方法,用于带着额头放置的自动化BC听力测量.
  • 集成的自动反侧面罩,遮效应补偿,以及额头-头骨的纠正.
  • 与手动常规BC听力测试对比的评估性能和评估的测试-重新测试可靠性.

主要成果:

  • 在自动化ML听力测量和手动常规听力测量之间没有发现显著的性能差异.
  • 自动化ML-音频测量证明了高的测试-重新测试可靠性.
  • 在正常听力和听力受损的成年听众中,ML方法被证明有效.

结论:

  • 基于ML的自动BC听力测量与额头放置是传统方法的可行和可靠的替代方案.
  • 这种方法可以更有效,更准确地评估听力敏度.
  • 研究结果支持使用ML-听力测量用于广泛的成年人听力状态.