Introduction

: Auditory brainstem responses (ABRs) offer a unique opportunity to assess the neural integrity of the peripheral auditory nervous system in individuals presenting with listening difficulties. ABRs are typically recorded and analyzed by an audiologist who manually measures the timing and quality of the waveforms. The interpretation of ABRs requires considerable experience and training, and inappropriate interpretation can lead to incorrect judgments about the integrity of the system. Machine learning (ML) techniques may be a suitable approach to automate ABR interpretation and reduce human error.

Objectives

: The main objective of this paper was to identify a suitable ML technique to automate the analysis of ABR responses.

Methods

: ABR responses recorded during routine clinical assessment from 136 children being evaluated for auditory processing difficulties were analyzed using several common ML algorithms: Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Gradient Boosting (GB), Extreme Gradient Boosting (Xgboost), and Neural Networks (NN). A variety of signal feature extraction techniques were used to extract features from the ABR waveforms as inputs to the ML algorithms. Statistical significance testing and confusion matrices were used to identify the most robust model capable of accurately identifying neurological abnormalities present in ABRs.

Results

: Clinically significant features in the time-frequency representation of the signal were identified. The ML model trained using the Xgboost algorithm was identified as the most robust model with an accuracy of 92% compared to other models.

Conclusion

: The findings of the present study demonstrate that it is possible to develop accurate ML models to automate the process of analyzing ABR waveforms recorded at suprathreshold levels. There is currently no ML-based application to screen children with listening difficulties. Therefore, it is expected that this work will be translated into an evaluation tool that can be used by audiologists in the clinic. Furthermore, this work may aid future researchers in exploring ML paradigms to improve clinical test batteries used by audiologists in achieving accurate diagnoses.

中文翻译：

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机器学习模型对听觉加工障碍儿童记录的听觉脑干反应进行分类的比较

介绍

：听觉脑干反应（ABR）提供了一个独特的机会来评估听力有障碍的个体周围听觉神经系统的神经完整性。ABR通常由人工测量和分析波形时序和质量的听力学家记录和分析。ABR的解释需要大量的经验和培训，而不正确的解释可能导致对系统完整性的错误判断。机器学习（ML）技术可能是自动执行ABR解释并减少人为错误的合适方法。

目标

：本文的主要目的是确定一种合适的ML技术，以自动分析ABR反应。

方法

：使用几种常见的机器学习算法分析了常规临床评估过程中记录的136名儿童的ABR反应，这些儿童被评估为听觉处理困难：支持向量机（SVM），随机森林（RF），决策树（DT），梯度提升（GB），极端梯度增强（Xgboost）和神经网络（NN）。多种信号特征提取技术用于从ABR波形中提取特征，作为ML算法的输入。统计显着性测试和混淆矩阵用于确定能够准确识别ABR中存在的神经系统异常的最强大模型。

结果

：鉴定了信号的时频表示中的临床显着特征。与其他模型相比，使用Xgboost算法训练的ML模型被认为是最可靠的模型，其准确性为92％。

结论

：本研究的发现表明，有可能开发出准确的ML模型，以自动化分析以超阈值水平记录的ABR波形的过程。当前没有基于ML的应用程序来筛选有听力障碍的孩子。因此，可以预期，这项工作将被翻译成评估工具，可供诊所的听力学家使用。此外，这项工作可能会帮助未来的研究人员探索ML范例，以改进听力学家用来实现准确诊断的临床测试电池。