A computational approach to optimal deactivation of cochlear implant electrodes

人工耳蜗植入电极最佳停用的计算方法

• 批准号: 10578756
• 负责人: Elad Sagi
• 金额: $ 25.43万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10578756
• 关键词: Adoption; Affect; American; Auditory; Clinical; Cochlear Implants; Cochlear implant procedure; Computer Models; Cueing for speech; Data; Devices; Discrimination; Ear; Electrodes; Goals; Hearing; Implanted Electrodes; Individual; Left; Linear Models; Manufacturer; Measures; Modeling; Modernization; National Institute on Deafness and Other Communication Disorders; Noise; Outcome; Patients; Performance; Psychophysics; Recommendation; Resolution; Sensory; Speech; Speech Perception; Subjects Selections; Translating; Work; active control; clinical practice; computing resources; deaf; design; experience; functional restoration; hearing restoration; implantable device; implantation; improved; programs; simulation; sound; standard of care; success; uptake

PROJECT SUMMARY Cochlear implantation has successfully restored hearing to hundreds of thousands of individuals worldwide. However, speech understanding performance with CIs remains highly variable. Despite this variability in outcomes, CIs are typically programmed with default settings with only a few subject-specific programming parameters adjusted. This likely occurs because too many possible CI device setting combinations are available and data-driven guidance to achieve maximal benefit for an individual CI user is lacking. For example, most CIs are programmed with all viable electrodes active even though some electrodes may be poorly suited to convey speech information. Previous studies evaluated whether selective deactivation of some of these electrodes improved speech understanding, with limited success. However, these studies did not consider how speech information is represented across remaining electrodes, or the optimal number of electrodes to deactivate. The goal of the present study is to use computationally driven models of speech understanding in CI users to guide the search for which combination of active electrodes can yield the best speech understanding for a specific patient. Aim 1 is to quantify speech understanding and sound quality with model-recommended combinations of active electrodes compared to clinical standard-of-care settings, and compared to two control active electrode combinations. These alternative conditions will use the same number of electrodes as the model- recommended condition, but with electrodes selected in a similar way as previous studies. Subjects will have 1.5 months of regular use with each experimental active electrode conditions. Performance with experimental and clinical active electrode conditions will be compared using a repeated-measures design. It is hypothesized that the model-recommended condition will result in significantly better speech understanding than the other conditions. Aim 2 is to translate the model-driven recommendations from Aim 1 into practical guidance about how many (and possibly which) CI electrodes to deactivate. Subjects’ performance with the experimental and clinical active electrode conditions of Aim 1 will be used to build a hierarchical linear model. This model will relate performance with experimental active electrode settings to the following independent variables: number of active electrodes selected for subjects by the model, physical span of active electrodes, subjects’ speech- cue resolution with each experimental condition, and three demographic variables. It is hypothesized that subjects’ performance will be more strongly correlated with the number of active electrodes and subjects’ speech-cue resolution, and moderately correlated to physical span of active electrodes. Data obtained under the auspices of the current proposal will be foundational for larger studies to provide data- driven guidance for optimal fitting of CI devices.

项目摘要 人工耳蜗植入已成功地恢复了全球数十万个人的听力。 但是，使用CI的语音理解性能仍然很大。尽管有这种差异 结果，CI通常使用默认设置进行编程，只有几个特定于主题的编程 调整了参数。这种可能发生的可能是因为太多可能的CI设备设置组合是 缺乏可用的和数据驱动的指导，可为单个CI用户获得最大收益。为了 例如，大多数顺式都用所有可行的电极进行编程，即使某些电极可能是 不适合传达语音信息。先前的研究评估了某些的选择性停用 这些电极中的一部分提高了语音理解，成功的成功。但是，这些研究没有 考虑如何在剩余电子设备中表示语音信息，或者是最佳数量 电极停用。本研究的目的是使用计算驱动的语音模型 了解CI用户的理解以指导搜索主动电子的组合可以产生最佳 对特定患者的语音理解。 AIM 1是通过主动的模型组合来量化语音理解和声音质量 与临床标准设置相比，电极与两个对照主电极相比 组合。这些替代条件将使用与模型相同数量的电子 推荐条件，但以与以前的研究相似的方式选择电极。受试者会有 每种实验活动电极条件的经常使用1.5个月。实验性能 使用重复测量设计将比较临床活动电极条件。它是假设的 模型推荐的条件将导致比其他条件明显更好的语音理解 状况。 AIM 2是将AIM 1的模型驱动建议转化为有关的实用指导 有多少（可能以及可能的）CI电子停用。受试者在实验和 AIM 1的临床活动电极条件将用于构建分层线性模型。这个模型将 将性能与实验活动电极设置与以下自变量相关联：数字 通过模型选择受试者的主动电子，主动电子的物理跨度，受试者的语音 - 每个实验条件的提示分辨率和三个人口统计学变量。假设 受试者的性能将与主动电子和受试者的数量更密切相关 语音提示分辨率，并与活性电子的物理跨度相关。 在当前建议的主持下获得的数据将是基础，以提供较大的研究以提供数据 - CI设备最佳拟合的驱动指南。