Transient-evoked otoacoustic emission signals predicting outcomes of acute sensorineural hearing loss in patients with Ménière’s disease

Abstract

Background: Fluctuating hearing loss is characteristic of Ménière’s disease (MD) during acute episodes. However, no reliable audiometric hallmarks are available for counselling the hearing recovery possibility.

Aims/objectives: To find parameters for predicting MD hearing outcomes.

Material and methods: We applied machine learning techniques to analyse transient-evoked otoacoustic emission (TEOAE) signals recorded from patients with MD. Thirty unilateral MD patients were recruited prospectively after onset of acute cochleo-vestibular symptoms. Serial TEOAE and pure-tone audiogram (PTA) data were recorded longitudinally. Denoised TEOAE signals were projected onto the three most prominent principal directions through a linear transformation. Binary classification was performed using a support vector machine (SVM). TEOAE signal parameters, including signal energy and group delay, were compared between improved (PTA improvement: ≥15 dB) and nonimproved groups using Welch’s t-test.

Results: Signal energy did not differ (p = .64) but a significant difference in 1-kHz (p = .045) group delay was recorded between improved and nonimproved groups. The SVM achieved a cross-validated accuracy of >80% in predicting hearing outcomes.

Conclusions and significance: This study revealed that baseline TEOAE parameters obtained during acute MD episodes, when processed through machine learning technology, may provide information on outer hair cell function to predict hearing recovery.

Chinese abstract

背景：波动性听力损失是梅尼埃病（MD）急性发作期的特征。然而, 没有可靠的听力测量标记可用于确定听力恢复的可能性。

目的：寻找预测MD听力结果的参数。

材料与方法：应用机器学习技术来分析MD患者的瞬态诱发耳声发射（TEOAE）信号。30例单侧MD患者在出现急性耳蜗前庭症状后被招入此前瞻性研究。纵向记录连续TEOAE和纯音听力图（PTA）数据。去噪后的TEOAE信号通过线性变换投射到三个最显著的主方向上。采用支持向量机（SVM）进行二值分类。用韦尔奇t检验法比较了改进组（PTA改进≥15 dB）和未改进组的TEOAE信号参数, 包括信号能量和群组时延。

结果：改进组与非改进组之间的信号能量无显著性差异（p =0 .64）, 但两组间1-kHz（p =0 .045）群组时延有显著性差异。支持向量机在预测听力结果方面达到了80%以上的交叉验证准确率。

结论与意义：该研究发现, 通过机器学习技术处理急性MD发作期间获得的TEOAE基线参数, 可以提供外毛细胞功能信息, 预测听力恢复。

Keywords:

• Transient-evoked otoacoustic emission
• acute sensorineural hearing loss
• Ménière’s disease
• machine learning

Acknowledgement

Authors thank Mr. Daniel Wu’s help to organize the literatures.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is sponsored by the Ministry of Science and Technology, Taiwan. Project number: 107-2221-E-007-093-MY2.