深度学习模型用于预测听力值,基于扫描音调刺激频率的耳声发射
1Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
Ear and hearing
|November 22, 2023
概括
深度学习模型使用刺激频率音声发射 (SFOAEs) 准确预测听力值. 这些先进的模型优于传统方法,为听力损失提供了更好的诊断价值.
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
- 人工智能的人工智能
背景情况:
- 听力值预测对于诊断听力损失至关重要.
- 刺激频率耳声发射 (SFOAEs) 提供了对耳功能的一种非侵入性测量.
- 从SFOAEs来预测听力值的传统方法有局限性.
研究的目的:
- 开发和评估深度学习 (DL) 模型,用于定量预测听力值.
- 为了评估DL模型的性能,使用SFOAEs引起的扫描音调.
- 将DL模型与传统机器学习 (ML) 方法的有效性进行比较.
主要方法:
- 开发了四种DL模型 (CNN,CNN-KNN,CNN-SVM,CNN-RF) 来预测听力值.
- 模型使用SFOAE振幅光谱和信号噪声比频谱作为输入.
- 通过嵌套交叉验证,使用平均绝对误差和标准误差来评估性能.
主要成果:
- 在DL模型中,在测试频率上,最佳的平均绝对误差在5.22至6.06dB之间.
- 标准误差在7.27至8.55dB之间.
- 与传统的ML模型相比,所有开发的DL模型都表现出卓越的性能.
结论:
- 基于扫音SFOAE的DL模型可以以令人满意的准确度量化预测听力值.
- DL技术有效地捕捉到SFOAEs和听力值之间的复杂关系.
- 这些发现表明,SFOAEs在听力学中的诊断价值有可能得到增强.
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