Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
School of Medicine, Shanghai University, Shanghai, China.
Trends Hear. 2020 Jan-Dec;24:2331216520960053. doi: 10.1177/2331216520960053.
It is of clinical interest to estimate pure-tone thresholds from potentially available objective measures, such as stimulus-frequency otoacoustic emissions (SFOAEs). SFOAEs can determine hearing status (normal hearing vs. hearing loss), but few studies have explored their further potential in predicting audiometric thresholds. The current study investigates the ability of SFOAEs to predict hearing thresholds at octave frequencies from 0.5 to 8 kHz. SFOAE input/output functions and pure-tone thresholds were measured from 230 ears with normal hearing and 737 ears with sensorineural hearing loss. Two methods were used to predict hearing thresholds. Method 1 is a linear regression model; Method 2 proposed in this study is a back propagation (BP) network predictor built on the bases of a BP neural network and principal component analysis. In addition, a BP network classifier was built to identify hearing status. Both Methods 1 and 2 were able to predict hearing thresholds from 0.5 to 8 kHz, but Method 2 achieved better performance than Method 1. The BP network classifiers achieved excellent performance in determining the presence or absence of hearing loss at all test frequencies. The results show that SFOAEs are not only able to identify hearing status with great accuracy at all test frequencies but, more importantly, can predict hearing thresholds at octave frequencies from 0.5 to 8 kHz, with best performance at 0.5 to 4 kHz. The BP network predictor is a potential tool for quantitatively predicting hearing thresholds, at least at 0.5 to 4 kHz.
从潜在的客观测量指标(如刺激频率耳声发射(SFOAE))估计纯音阈值具有临床意义。SFOAE 可确定听力状况(正常听力与听力损失),但很少有研究探索其在预测听力阈值方面的进一步潜力。本研究调查了 SFOAE 在预测 0.5 至 8 kHz 倍频程听力阈值方面的能力。从 230 只正常听力耳和 737 只感音神经性听力损失耳中测量了 SFOAE 的输入/输出函数和纯音阈值。使用两种方法预测听力阈值。方法 1 是线性回归模型;方法 2 是本研究中提出的基于反向传播(BP)神经网络和主成分分析构建的 BP 网络预测器。此外,还构建了一个 BP 网络分类器来识别听力状况。方法 1 和方法 2 都能够预测 0.5 至 8 kHz 的听力阈值,但方法 2 的性能优于方法 1。BP 网络分类器在确定所有测试频率下是否存在听力损失方面表现出优异的性能。结果表明,SFOAE 不仅能够非常准确地识别所有测试频率的听力状况,而且更重要的是,能够预测 0.5 至 8 kHz 倍频程的听力阈值,在 0.5 至 4 kHz 时性能最佳。BP 网络预测器是一种潜在的定量预测听力阈值的工具,至少在 0.5 至 4 kHz 时是这样。