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Comparison between NRT-based MAPs and behaviourally measured MAPs at different stimulation rates--a multicentre
L Craddock1, H Cooper, P van de Heyning
1Midlands Adult Cochlear Implant Programme, UK.
Cochlear Implants International
|September 16, 2008
Summary
This study compared NRT-based MAPs and behavioral MAPs in cochlear implant users. Results showed no significant difference in speech discrimination performance between the two programming methods.
Area of Science:
- Audiology
- Neuroscience
- Biomedical Engineering
Background:
- Cochlear implant (CI) technology aims to restore hearing by bypassing damaged cochlear structures.
- Electrically Evoked Compound Action Potential (ECAP) measurements are crucial for programming CI devices.
- Neural Response Telemetry (NRT) and behavioral mapping (MAP) are key techniques for CI fitting.
Purpose of the Study:
- To compare the performance of NRT-based MAPs with behavioral MAPs in adult cochlear implant recipients.
- To investigate the accuracy of predicted T/C levels using an NRT-based correction factor.
- To evaluate T/C levels at different stimulation rates in behavioral MAPs against NRT-based predictions.
Main Methods:
- A multicentre trial involving 17 adult subjects using cochlear implants.
- Development of an NRT-based MAP utilizing a correction factor based on ECAP thresholds (T-NRT).
- Evaluation of speech discrimination using CNC words and sentences for both NRT-based and behavioral MAPs.
Main Results:
- Variations in T/C (Threshold/Comfort) levels were observed across all tested MAPs.
- No statistically significant differences were found in speech discrimination scores between behavioral and NRT-based MAPs.
- The study assessed T/C levels in behavioral MAPs at varying stimulation rates.
Conclusions:
- NRT-based MAPs and behavioral MAPs demonstrate comparable performance in speech discrimination for adult cochlear implant users.
- The NRT-based approach, while showing T/C level variations, did not lead to a significant performance deficit.
- Further research may explore optimal NRT-based programming strategies and their long-term effects.

