Wimalarathna Hasitha, Ankmnal-Veeranna Sangamanatha, Allan Chris, Agrawal Sumit K, Allen Prudence, Samarabandu Jagath, Ladak Hanif M
Department of Electrical & Computer Engineering, Western University, London, Ontario, Canada; National Centre for Audiology, Western University, London, Ontario, Canada.
National Centre for Audiology, Western University, London, Ontario, Canada.
Comput Methods Programs Biomed. 2021 Mar;200:105942. doi: 10.1016/j.cmpb.2021.105942. Epub 2021 Jan 17.
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.
The main objective of this paper was to identify a suitable ML technique to automate the analysis of ABR responses recorded as a part of the electrophysiological testing in the Auditory Processing Disorder clinical test battery.
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.
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.
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.
听觉脑干反应（ABR）为评估有听力困难个体的外周听觉神经系统的神经完整性提供了一个独特的机会。ABR通常由听力学家进行记录和分析，他们手动测量波形的时间和质量。ABR的解读需要相当多的经验和训练，而不恰当的解读可能导致对系统完整性的错误判断。机器学习（ML）技术可能是一种合适的方法来实现ABR解读的自动化并减少人为误差。
本文的主要目的是确定一种合适的ML技术，以实现对作为听觉处理障碍临床测试电池中电生理测试一部分所记录的ABR反应的自动化分析。
使用几种常见的ML算法：支持向量机（SVM）、随机森林（RF）、决策树（DT）、梯度提升（GB）、极端梯度提升（Xgboost）和神经网络（NN），对在常规临床评估期间从136名因听觉处理困难而接受评估的儿童中记录的ABR反应进行分析。使用了各种信号特征提取技术从ABR波形中提取特征，作为ML算法的输入。使用统计显著性检验和混淆矩阵来确定能够准确识别ABR中存在的神经学异常的最稳健模型。
确定了信号时频表示中的临床显著特征。与其他模型相比，使用Xgboost算法训练的ML模型被确定为最稳健的模型，准确率为92%。
本研究的结果表明，有可能开发出准确的ML模型，以实现对超阈值水平记录的ABR波形分析过程的自动化。目前尚无基于ML的应用程序来筛查有听力困难的儿童。因此，预计这项工作将转化为一种可供听力学家在临床使用的评估工具。此外，这项工作可能有助于未来的研究人员探索ML范式，以改进听力学家用于实现准确诊断的临床测试电池。
