Assessment of Auditory Function and Predictive Model Construction in Children with Obstructive Sleep Apnea

ZOU Yi; LU Ying-xia; YUWEN Qi; ZHANG Xu

doi:10.3969/j.issn.1672-4933.2025.03.012

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Assessment of Auditory Function and Predictive Model Construction in Children with Obstructive Sleep Apnea

Clinical Research | 更新时间：2026-07-11

- Assessment of Auditory Function and Predictive Model Construction in Children with Obstructive Sleep Apnea
- Chinese Scientific Journal of Hearing and Speech Rehabilitation Vol. 23, Issue 3, Pages: 276-280(2025)
- 作者机构：
  
  1.首都医科大学生物医学工程学院北京 100069
  2.首都医科大学附属首都儿童医学中心北京 100020
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1672-4933.2025.03.012
  CLC： R764.5
- Received：24 January 2025，
  
  Published：15 May 2025
- 稿件说明：
移动端阅览
邹怡,陆颖霞,宇文奇等.阻塞性睡眠呼吸暂停儿童听功能评估及模型构建[J].中国听力语言康复科学杂志,2025,23(03):276-280.

ZOU Yi,LU Ying-xia,YUWEN Qi,et al.Assessment of Auditory Function and Predictive Model Construction in Children with Obstructive Sleep Apnea[J].Chinese Scientific Journal of Hearing and Speech Rehabilitation,2025,23(03):276-280.
邹怡,陆颖霞,宇文奇等.阻塞性睡眠呼吸暂停儿童听功能评估及模型构建[J].中国听力语言康复科学杂志,2025,23(03):276-280. DOI： 10.3969/j.issn.1672-4933.2025.03.012.

ZOU Yi,LU Ying-xia,YUWEN Qi,et al.Assessment of Auditory Function and Predictive Model Construction in Children with Obstructive Sleep Apnea[J].Chinese Scientific Journal of Hearing and Speech Rehabilitation,2025,23(03):276-280. DOI： 10.3969/j.issn.1672-4933.2025.03.012.

摘要

目的

通过对阻塞性睡眠呼吸暂停（obstructive sleep apnea，OSA）儿童的听力效果进行评估，建立基于机器学习方法的OSA儿童听力损失预测模型。

方法

选择经多导睡眠监测诊断为OSA的儿童为研究对象，其中轻度组64耳、中重度组96耳。同期体检的健康儿童为对照组，共94耳。对所有受试者进行听功能评估。采用支持向量机、逻辑回归、朴素贝叶斯3种机器学习方法建立OSA儿童听力损失预测模型并选出最优模型。

结果

①3组气导听阈在0.25～2 kHz差异无统计学意义；4 kHz及以上高频和扩展高频差异具有统计学意义（

0.05）。②3组畸变产物耳声发射（DPOAE）幅值在0.75～3 kHz差异无统计学意义；在4、6、8 kHz差异具有统计学意义（

0.05）。③3组ABRⅠ波、Ⅲ波潜伏期及Ⅰ～Ⅲ波间期差异无统计学意义；Ⅴ波潜伏期及Ⅲ～Ⅴ、Ⅰ～Ⅴ波间期差异具有统计学意义（

0.05）。④基于朴素贝叶斯方法构建的OSA儿童听力损失模型为最优预测模型。⑤模型特征重要性排名显示，重要的影响因素为最低血氧饱和度和氧降指数。

结论

儿童OSA与听力损失存在显著关联，低氧血症的严重程度是导致OSA儿童听力损失的关键因素。

Abstract

Objective

This study aims to assess the auditory function in children with obstructive sleep apnea (OSA) and to construct a machine learning-based predictive model for hearing loss in pediatric OSA patients.

Methods

Children diagnosed with OSA through polysomnography (PSG) were enrolled as study subjects

categorized into a mild group (64 ears) and a moderate-to-severe group (96 ears). A control group of 94 ears from healthy children undergoing routine physical examinations during the same period was also included. Comprehensive auditory function assessments were conducted on all the participants. Three machine learning methods:Support Vector Machine

Logistic Regression

and Naive Bayes were employed to construct predictive models for hearing loss in children with OSA. The optimal model was selected based on performance metrics.

Results

①No significant differences were observed in hearing thresholds at 0.25-2 kHz across the three groups. However

significant differences were noted at frequencies ≥4 kHz and in the extended high-frequency range (

0.05).②Distortion product otoacoustic emission (DPOAE) amplitudes showed no significant differences at 0.75-3 kHz but exhibited significant variations at 4

6 and 8 kHz (

0.05).③Auditory brainstem response (ABR) measurements revealed no significant differences in Wave I and III latencies or the I-III interpeak interval. However

significant differences were observed in Wave V latency and the III-V and I-V interpeak intervals (

0.05).④The Naive Bayes-based model demonstrated superior performance in predicting hearing loss in children with OSA.⑤Feature importance analysis identified the lowest oxygen saturation and oxygen desaturation index as the most influential predictors of hearing loss.

Conclusion

This study establishes a significant association between OSA and hearing loss in children

with the severity of hypoxemia emerging as a critical determinant. The Naive Bayes-based predictive model offers a robust tool for identifying hearing loss risk in pediatric OSA patients

highlighting the importance of early intervention and monitoring.

关键词

Keywords

references

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