McKearney Richard M, Simpson David M, Bell Steven L
Institute of Sound and Vibration Research, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK.
Int J Audiol. 2025 Jul;64(7):766-771. doi: 10.1080/14992027.2024.2404537. Epub 2024 Oct 3.
To compare the performance of a selection of machine learning algorithms, trained to label peaks I, III, and V of the auditory brainstem response (ABR) waveform. An additional algorithm was trained to provide a confidence measure related to the ABR wave latency estimates.
Secondary data analysis of a previously published ABR dataset. Five types of machine learning algorithm were compared within a nested k-fold cross-validation procedure.
A set of 482 suprathreshold ABR waveforms were used. These were recorded from 81 participants with audiometric thresholds within normal limits.
A convolutional recurrent neural network (CRNN) outperformed the other algorithms evaluated. The algorithm labelled 95.9% of ABR waves within ±0.1 ms of the target. The mean absolute error was 0.025 ms, averaged across the outer validation folds of the nested cross-validation procedure. High confidence levels were generally associated with greater wave-labelling accuracy.
Machine learning algorithms have the potential to assist clinicians with ABR interpretation. The present work identifies a promising machine learning approach, but any algorithm to be used in clinical practice would need to be trained on a large, accurately labelled, heterogeneous dataset and evaluated in clinical settings in follow-on work.
比较一系列经过训练用于标记听觉脑干反应（ABR）波形中I、III和V波峰的机器学习算法的性能。还训练了一种额外的算法，以提供与ABR波潜伏期估计相关的置信度测量。
对先前发表的ABR数据集进行二次数据分析。在嵌套k折交叉验证程序中比较了五种类型的机器学习算法。
使用了一组482个阈上ABR波形。这些波形是从81名听力阈值在正常范围内的参与者中记录的。
卷积递归神经网络（CRNN）的表现优于其他评估算法。该算法在目标值±0.1毫秒范围内标记了95.9%的ABR波。在嵌套交叉验证程序的外部验证折中平均，平均绝对误差为0.025毫秒。高置信度水平通常与更高的波标记准确性相关。
机器学习算法有潜力协助临床医生解读ABR。本研究确定了一种有前景的机器学习方法，但任何用于临床实践的算法都需要在一个大型、准确标记的异质数据集上进行训练，并在后续工作的临床环境中进行评估。
