Literature DB >> 24209981

An objective auditory measure to assess speech recognition in adult cochlear implant users.

C Turgeon1, L Lazzouni1, F Lepore1, D Ellemberg2.   

Abstract

OBJECTIVE: To verify if a mismatch negativity (MMN) paradigm based on speech syllables can differentiate between good and poorer cochlear implant (CI) users on a speech recognition task.
METHODS: Twenty adults with a CI and 11 normal hearing adults participated in the study. Based on a speech recognition test, ten CI users were classified as good performers and ten as poor performers. We measured the MMN with /da/ as the standard stimulus and /ba/ and /ga/ as the deviants. Separate analyses were conducted on the amplitude and latency of the MMN.
RESULTS: A MMN was evoked by both deviant stimuli in all normal hearing participants and in well performing CI users, with similar amplitudes for both groups. However, the amplitude of the MMN was significantly reduced for the poorer CI users compared to the normal hearing group and the good CI users. The latency was longer for both groups of cochlear implant users. A bivariate correlation showed a significant positive correlation between the speech recognition score and the amplitude of the MMN.
CONCLUSIONS: The MMN can distinguish between CI users who have good versus poor speech recognition as assessed with conventional tasks. SIGNIFICANCE: Our findings suggest that the MMN can be use to assess speech recognition proficiency in CI users who cannot be tested with regular speech recognition tasks, like infants and other non-verbal populations.
Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Auditory evoked potential; Cochlear implant; Mismatch negativity; Speech perception

Mesh:

Year:  2013        PMID: 24209981     DOI: 10.1016/j.clinph.2013.09.035

Source DB:  PubMed          Journal:  Clin Neurophysiol        ISSN: 1388-2457            Impact factor:   3.708


  5 in total

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  5 in total

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