深度学习模型用于预测听力值,基于联合刺激频率的音响发射和扭曲产品的音响发射
Hearing research
|July 6, 2025
概括
这项研究开发了一种高效的深度学习模型,将刺激频率的音声发射 (SFOAEs) 和扭曲产物OAEs (DPOAEs) 整合起来,以准确地预测听力值. 双OAE模型对客观的听力损失诊断非常有希望.
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:
- 刺激频率音声发射 (SFOAEs) 和扭曲产物音声发射 (DPOAEs) 来自不同的耳机制.
- 无论是SFOAEs还是DPOAEs都可以描述听力损失,但它们用于听力值预测的联合使用是新的.
研究的目的:
- 开发一个高效的深度学习 (DL) 模型,整合 SFOAEs 和 DPOAEs,用于定量听力值预测.
- 与单个OAE和传统机器学习模型相比,评估这种双OAEDL模型的准确性和效率.
主要方法:
- 使用卷积神经网络 (CNN) 和循环神经网络 (RNN) 构建了一个深度学习模型.
- 输入特征包括SFOAEs和DPOAEs的振幅和信号噪声比谱.
- 模型在94个正常听力和401个感觉神经听力损失耳朵的数据上进行了训练和交叉验证,这些耳朵的八度频率从0.5到8kHz.
主要成果:
- 双OAE DL模型实现了低平均绝对误差 (MAE),在测试频率中范围为3.83至5.17dB.
- 它显著优于单个OAE DL模型和基线机器学习方法.
- 一个效率优化的版本将测试时间缩短到每个人大约15分钟,同时保持准确性.
结论:
- 集成的SFOAE和DPOAE DL模型为听力值预测提供了最先进的准确性.
- 它的优化效率支持其作为客观听力损失诊断的实用临床工具的潜力.
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