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.
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.
Authors: Klaus Schuerch; Wilhelm Wimmer; Adrian Dalbert; Christian Rummel; Marco Caversaccio; Georgios Mantokoudis; Stefan Weder Journal: Front Neurol Date: 2022-08-29 Impact factor: 4.086