Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

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Abstract

Cochlear implant (CI) provision is the most effective clinical treatment to restore hearing performance in individuals with profound sensorineural hearing loss (SNHL). It has been successful in providing improved speech perception outcomes, especially in quiet environments. However, speech perception performance within complex environments, lexical tone recognition, and music perception have been shown to only improve with newer fine structure coding strategies or related techniques. Therefore, the methods used to assess hearing performance in noisy environments, lexical tone recognition, and music perception are of vital importance. These assessments must reflect the postoperative outcomes and also provide guidance for the programming, rehabilitation, and application of new coding strategies. In this study, hearing performance in simple and complex situations was evaluated before and after upgrading to a fine structure strategy. The participants were a cohort of Mandarin-speaking adolescents, who were experienced CI users. The comprehensive clinical workflow involved assessments of speech in quiet conditions, speech in noisy conditions, lexical tone recognition, and music perception. This battery of tests is explained in detail, from the coding strategy to the test methods, including the test process, environment, device, material, and order. The details that require special attention are discussed, such as the position of the participants, the angle of the loudspeaker, the intensity of the sound, the noise type, the practice test, and the way of answering questions. Each test step, method, and material for speech, lexical tone, and music perception is presented in detail. Finally, the clinical results are discussed.

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Wang, R., Zhao, N., Luo, J., Chao, X., Fan, Z., Wang, H., & Xu, L. (2023). Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages. Journal of Visualized Experiments, 2023(193). https://doi.org/10.3791/64552

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