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. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and text of this article on the journal's Web site (www.ear-hearing.com). ACKNOWLEDGMENTS: The authors would also like to thank Jo Brooks and Sara Al-Hanbali for data collection. This article presents independent research funded by the Oticon Fonden and by the National Institute for Health Research (NIHR) under its Research for Patient Benefit (RfPB) Programme (Grant Reference Number PB-PG-0214-33009). K.J.M. was supported by the NIHR Manchester Biomedial Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. M.A.C. and D.M.S. contributed toward algorithm design and data analysis, S.L.B., K.J.M., A.V., M.A.S., J.M.H., and L.B.S. contributed toward project conception and/or experiment design and/or data acquisition and interpretation. All authors contributed toward the drafting and critical revision of the manuscript. The authors declare no conflicts of interest to disclose. Received September 27, 2019; accepted August 12, 2020. Address for correspondence: Michael Alexander Chesnaye, Institute of Sound and Vibration Research, Faculty of Engineering and the Environment, University of Southampton, United Kingdom. E-mail: mac1r19@soton.ac.uk Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.
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