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: 10430378
• 负责人: Elad Sagi
• 金额: $ 21.19万
• 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 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10430378
• 关键词: Adoption; Affect; American; Auditory; Clinical; Cochlear Implants; Cochlear implant procedure; Computer Models; Cueing for speech; Data; Devices; Discrimination; Ear; Electrodes; Foundations; Goals; Hearing; Implanted Electrodes; Individual; Left; Linear Models; Manufacturer Name; Measures; Modeling; Modernization; National Institute on Deafness and Other Communication Disorders; Noise; Outcome; Patients; Performance; Psychophysics; Recommendation; Resolution; Sensory; Speech; Speech Perception; Translating; Work; active control; clinical practice; computing resources; deaf; design; experience; 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.

项目概要 人工耳蜗植入已成功帮助全球数十万人恢复听力。 然而，尽管存在这种差异，CI 的语音理解性能仍然存在很大差异。 结果，CI 通常使用默认设置进行编程，只有少数特定于主题的编程 发生这种情况的原因可能是调整了太多可能的 CI 设备设置组合。 缺乏可用且数据驱动的指导来为单个 CI 用户实现最大利益。 例如，大多数 CI 都被编程为所有可行的电极都处于活动状态，即使某些电极可能处于活动状态 以前的研究评估了是否选择性停用某些功能，不太适合传达语音信息。 这些电极的使用改善了语音理解，但取得的成功有限。 考虑如何在剩余电极上表示语音信息，或者最佳数量 本研究的目标是使用计算驱动的语音模型。 CI 用户的理解，以指导寻找哪种有源电极组合可以产生最佳效果 对特定患者的言语理解。 目标 1 是通过模型推荐的主动激活组合来量化语音理解和声音质量。 电极与临床护理标准设置的比较，以及与两个对照有源电极的比较 这些替代条件将使用与模型相同数量的电极。 推荐的条件，但电极的选择方式与之前的研究相似。 1.5 个月定期使用各实验活性电极的性能与实验结果。 将使用重复测量设计来比较临床活性电极条件。 模型推荐的条件将导致比其他条件明显更好的语音理解 目标 2 是将目标 1 的模型驱动建议转化为有关的实际指导。 需要停用多少个（可能是哪些）CI 电极。 目标 1 的临床活性电极条件将用于构建分层线性模型。 实验活性电极设置的性能与以下自变量相关：数量 模型为受试者选择的有源电极的数量、有源电极的物理跨度、受试者的言语- 每个实验条件和三个人口统计变量的提示分辨率。 受试者的表现将与活跃电极的数量和受试者的 语音提示分辨率，并与有源电极的物理跨度适度相关。 在当前提案的支持下获得的数据将为更大规模的研究提供基础，以提供数据 - CI 设备最佳适配的驱动指导。