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Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

使用机器学习对听觉脑干反应进行自动波标记.

Richard M McKearney¹, David M Simpson¹, Steven L Bell¹

¹Institute of Sound and Vibration Research, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK.

International journal of audiology

|October 4, 2024

概括

一个卷积循环神经网络 (CRNN) 准确标记了听觉脑干响应 (ABR) 波形,显示了临床解释的潜力. 高可信度得分与精确的波标记准确度相关.

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
机器学习机器学习

背景情况:

听觉脑干反应 (ABR) 分析对于诊断听觉通路障碍至关重要.
准确标记ABR波形峰值 (I,III,V) 对于可靠的解释至关重要.
目前的ABR解释可能是主观和耗时的.

研究的目的:

为了比较各种机器学习算法的性能,以标记关键的ABR波形峰值.
开发一种算法,为ABR波延迟估计提供可信度指标.

主要方法:

发表的ABR数据集的二次分析,包括81名参与者的482个超值波形.
五个机器学习算法的比较,使用嵌套的k-fold交叉验证程序.
训练一个额外的算法来估计对ABR波延迟的信心.

主要成果:

一个卷积循环神经网络 (CRNN) 在其他评估的算法中表现出优异的性能.
在标记ABR波时,CRNN达到95.9%的准确度,距离目标±0.1毫秒.
波延迟估计的平均绝对误差为0.025毫秒,高置信度与更高的准确性有关.

结论:

关键词:

听觉脑干反应的响应自动化分析自动化分析电力生理学电力生理学唤起了潜在的潜力.机器学习是机器学习.

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机器学习,特别是CRNN,显示出有很大的潜力来帮助临床医生在ABR解释.
开发的算法为自动化ABR分析提供了有希望的结果.
进一步的研究涉及大型,多样化的数据集和临床验证是必要的,以实现现实世界的应用.