Automated cortical auditory response detection strategy

Abstract

Objective: This study describes a new automated strategy to determine the detection status of an electrophysiological response.

Design: Response, noise and signal-to-noise ratio of the cortical auditory evoked potential (CAEP) were characterised. Detection rules were defined: when to start testing, when to conduct subsequent statistical tests using residual noise as an objective criterion, and when to stop testing.

Study sample: Simulations were run to determine optimal parameters on a large combined CAEP data set collected in 45 normal-hearing adults and 17 adults with hearing loss.

Results: The proposed strategy to detect CAEPs is fully automated. The first statistical test is conducted when the residual noise level is equal to or smaller than 5.1 µV. The succeeding Hotelling’s T² statistical tests are conducted using pre-defined residual noise levels criteria ranging from 5.1 to 1.2 µV. A rule was introduced allowing to stop testing before the maximum number of recorded epochs is reached, depending on a minimum p-value criterion.

Conclusion: The proposed framework can be applied to systems which involves detection of electrophysiological responses in biological systems containing background noise. The proposed detection algorithm which optimise sensitivity, specificity, and recording time has the potential to be used in clinical setting.

Keywords:

• Electrophysiology
• objective response detection
• automated algorithm
• cortical auditory evoked potentials
• residual noise level criteria

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Acknowledgements

We thank Professor Robert Cowan and the two reviewers for helpful comments on the manuscript. The experimental data used for this research were collected while the authors worked at the National Acoustic Laboratories.

Disclosure statement

The authors have been involved with the development of HEARLab and its modules, but do not receive financial benefits from its sale.

Additional information

Funding

This work is financially supported by the NSW Ministry of Health, Hearing CRC, the Australian Department of Health, the NIHR Manchester Biomedical Research Centre and Macquarie University.