Manta Ourania, Sarafidis Michail, Vasileiou Nikolaos, Schlee Winfried, Consoulas Christos, Kikidis Dimitris, Vassou Evgenia, Matsopoulos George K, Koutsouris Dimitrios D
Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece.
Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany.
Brain Sci. 2022 Dec 6;12(12):1675. doi: 10.3390/brainsci12121675.
Auditory evoked potentials (AEPs) are brain-derived electrical signals, following an auditory stimulus, utilised to examine any obstructions along the brain neural-pathways and to diagnose hearing impairment. The clinical evaluation of AEPs is based on the measurements of the latencies and amplitudes of waves of interest; hence, their identification is a prerequisite for AEP analysis. This process has proven to be complex, as it requires relevant clinical experience, and the existing software for this purpose has little practical use. The aim of this study was the development of two automated annotation tools for ABR (auditory brainstem response)- and AMLR (auditory middle latency response)-tests. After the acquisition of 1046 raw waveforms, appropriate pre-processing and implementation of a four-stage development process were performed, to define the appropriate logical conditions and steps for each algorithm. The tools' detection and annotation results, regarding the waves of interest, were then compared to the clinicians' manual annotation, achieving match rates of at least 93.86%, 98.51%, and 91.51% respectively, for the three ABR-waves of interest, and 93.21%, 92.25%, 83.35%, and 79.27%, respectively, for the four AMLR-waves. The application of such tools in AEP analysis is expected to assist towards an easier interpretation of these signals.
听觉诱发电位（AEPs）是听觉刺激后源自大脑的电信号，用于检查大脑神经通路中的任何障碍并诊断听力障碍。AEPs的临床评估基于对感兴趣波的潜伏期和振幅的测量；因此，识别这些波是AEP分析的先决条件。事实证明，这个过程很复杂，因为它需要相关的临床经验，而现有的用于此目的的软件几乎没有实际用途。本研究的目的是开发两种用于听性脑干反应（ABR）和听性中潜伏期反应（AMLR）测试的自动注释工具。在获取1046个原始波形后，进行了适当的预处理并实施了一个四阶段的开发过程，以确定每种算法的适当逻辑条件和步骤。然后将工具关于感兴趣波的检测和注释结果与临床医生的手动注释进行比较，对于三个感兴趣的ABR波，匹配率分别至少为93.86%、98.51%和91.51%，对于四个AMLR波，匹配率分别为93.21%、92.25%、83.35%和79.27%。预计将此类工具应用于AEP分析有助于更轻松地解释这些信号。
