Automatic detection of eCAP thresholds

Automatic detection of eCAP thresholds: Precision and accuracy of different methods

Schupp, Eleen (TU Delft Mechanical, Maritime and Materials Engineering)

Briaire, J.J. (mentor)
Biesheuvel, J.D. (mentor)
Hendriks, R.C. (graduation committee)

Delft University of Technology
Universiteit Leiden
Erasmus Universiteit Rotterdam

Technical Medicine

2023-05-09

When a person suffers from severe to profound hearing loss, a cochlear implant (CI) can aid in restoring auditory perception and speech comprehension. To obtain good speech comprehension, fitting of a CI to the user’s specific characteristics is crucial. Fitting can be a time-consuming process which requires the cooperation of a CI user and is dependent on the used methods (e.g., T-level measurements). Besides transmitting electrical stimuli, a CI can also record the response of the auditory nerve fibres to a stimulus. This response is called the electrically evoked compound action potential (eCAP). eCAP responses can be measured objectively without a user’s active cooperation and could potentially aid in the fitting of a CI. For this purpose, the eCAP thresholds are of main interest. eCAP thresholds can be determined manually by a clinical specialist, or automatically by an automatic threshold detection method. Automatic eCAP threshold detection can therefore be of aid in a completely objective and uniform CI fitting method. The goal of this study was to compare different automatic eCAP threshold detection methods (in combination with different averaging methods and different artefact reduction methods) based on the precision and accuracy of these methods.
Five different automatic eCAP threshold detection methods have been examined in this study: sigmoid amplitude growth function (AGF), linear AGF, signal-to-noise ratio (SNR), cross-covariance between adjacent levels and cross-covariance with maximum level. The two different averaging methods that have been examined are standard averaging (SA) and FineGrain averaging (FG), the two different artefact reduction methods are alternating polarity (AP) and forward masking (FM). In total, 20 different combinations have been examined. The success rates of these 20 combinations have been determined, threshold confidence intervals (TCIs) were calculated as a measure of precision and the correlations between eCAP thresholds and T-levels were determined as a measure of accuracy of the different (combinations of) methods.
The combination of FG FM resulted in the highest success rates for different threshold detection methods, and the threshold detection method SNR had the overall highest success rates. A two-way ANOVA revealed that both artefact reduction/averaging method and threshold detection method have a significant effect on the TCIs. The combination of FG FM had the best resultsregarding the TCIs, and the sigmoid AGF threshold detection method was the threshold detection method with the lowest mean TCI. A similar two-way ANOVA was performed for the correlation between eCAP thresholds and T-levels, revealing the same results as for the TCIs that both artefact reduction/averaging method and threshold detection method have a significant effect on the correlation coefficients. FG FM was again the best performing combination, and the sigmoid AGF threshold detection method resulted in the highest correlation coefficients.
Based on these results, it can be stated that the FG FM combination for averaging and artefact reduction was the overall best combination. When comparing the different automatic threshold detection methods, the sigmoid AGF method resulted in eCAP thresholds with the highest precision and accuracy. Future research should focus on obtaining more data, further refinements of the different automatic eCAP threshold detection methods and the use of the determined eCAP thresholds in the clinical fitting of a CI.

Cochlear Implants
eCAP measurements
Automatic threshold detection
Precision
Accuracy

http://resolver.tudelft.nl/uuid:efe493e6-e55c-412a-a883-cf9921c28566

Student theses

master thesis

© 2023 Eleen Schupp