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Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice
Published on: May 10, 2019
Can subject-specific single-fibre electrically evoked auditory brainstem response data be predicted from a model?
Tiaan K Malherbe1, Tania Hanekom, Johan J Hanekom
1Bioengineering, Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa.
Medical Engineering & Physics
|October 2, 2012
Summary
This study developed a subject-specific computational model for cochlear implants to predict neural excitation. The model shows promise for understanding individual hearing variations and optimizing device settings.
Area of Science:
- Biomedical Engineering
- Auditory Neuroscience
- Computational Modeling
Background:
- Inter-subject variability in cochlear implant perception is significant.
- Understanding peripheral factors influencing perception is crucial for personalized device fitting.
- Computational models offer a potential tool for predicting subject-specific physiological responses.
Purpose of the Study:
- To develop a method for creating subject-specific computational models of cochlear implants.
- To assess the accuracy of these models in predicting peripheral neural excitation.
- To explore the potential for clinical translation in optimizing cochlear implant parameters.
Main Methods:
- Constructed a subject-specific computational model for a guinea pig cochlear implant.
- Collected single-fiber electrically evoked auditory brainstem responses (ABRs) from the inferior colliculus.
- Compared model predictions of neural excitation with experimental ABR data.
Main Results:
- The model accurately predicted threshold frequency location, spatial spread for bipolar/tripolar stimulation, and relative electrode thresholds.
- Absolute thresholds and spatial spread for monopolar stimulation were not accurately predicted.
- Model predictions showed good correlation with experimental data for specific stimulation configurations.
Conclusions:
- Subject-specific computational models can predict certain aspects of peripheral neural excitation in cochlear implant users.
- The developed method shows potential for translation to human models and clinical applications.
- Further model improvements are needed to enhance accuracy for monopolar stimulation and absolute thresholds.
