Efficient Detection of Cortical Auditory Evoked Potentials in Adults Using Bootstrapped Methods

Abstract

Background: Statistical detection methods are useful tools for assisting clinicians with cortical auditory evoked potential (CAEP) detection, and can help improve the overall efficiency and reliability of the test. However, many of these detection methods rely on parametric distributions when evaluating test significance, and thus make various assumptions regarding the electroencephalogram (EEG) data. When these assumptions are violated, reduced test sensitivities and/or increased or decreased false-positive rates can be expected. As an alternative to the parametric approach, test significance can be evaluated using a bootstrap, which does not require some of the aforementioned assumptions. Bootstrapping also permits a large amount of freedom when choosing or designing the statistical test for response detection, as the distributions underlying the test statistic no longer need to be known prior to the test. Objectives: To improve the reliability and efficiency of CAEP-related applications by improving the specificity and sensitivity of objective CAEP detection methods. Design: The methods included in the assessment were Hotelling’s T2 test, the Fmp, four modified q-sample statistics, and various template-based detection methods (calculated between the ensemble coherent average and some predefined template), including the correlation coefficient, covariance, and dynamic time-warping (DTW). The assessment was carried out using both simulations and a CAEP threshold series collected from 23 adults with normal hearing. Results: The most sensitive method was DTW, evaluated using the bootstrap, with maximum increases in test sensitivity (relative to the conventional Hotelling’s T2 test) of up to 30%. An important factor underlying the performance of DTW is that the template adopted for the analysis correlates well with the subjects’ CAEP. Conclusion: When subjects’ CAEP morphology is approximately known before the test, then the DTW algorithm provides a highly sensitive method for CAEP detection.

Objectification of intracochlear electrocochleography using machine learning

“…Hotelling's T 2 test showed the highest sensitivity of our tested algorithms. This high sensitivity is also known from other research ( 27 – 29 ). However, in order to achieve good results with the Hotelling T 2 method, the signal must be free of artifacts and baseline wander.…”

“…This results in TVMs with zero amplitude (similar to noise). If the TVMs are too short, the robustness and thus the test sensitivity decreases (overfitting) ( 29 , 39 ).…”

“…Based on Hotelling's T 2 method described by Golding et al and Chesnaye et al for objective detection of CAEP signals, we adapted the method to ECochG signals ( 27 , 29 ). The Hotelling's T 2 test for one sample is a multivariate extension of the Student's t -test ( 39 , 40 ).…”

“…For example, for auditory brainstem responses (ABR), correlation analysis is used ( 25 , 26 ). In the evaluation of cortical auditory evoked potentials (CAEP), Hotelling's T 2 test has yielded a sensitivity at least comparable to that of visual inspection ( 27 – 29 ). In other medical disciplines (i.e., identification of cardiac arrhythmias in electrocardiograms, ECGs), deep learning (DL) strategies could be successfully implemented ( 30 – 33 ).…”

Modified T2 Statistics for Improved Detection of Aided Cortical Auditory Evoked Potentials in Hearing-Impaired Infants

“…Various objective methods have therefore been proposed to assist the clinicians with this task, and improve the accuracy and efficiency of the test. However, the challenges for visual CAEP inspection arising from the variability in infant responses also impact objective detection methods: infant responses can be considerably different from adult responses, and template matching methods that performed best in adults ( Chesnaye et al, 2021a ) are unlikely to work well in this young population.…”