A computational approach to optimal deactivation of cochlear implant electrodes

项目总结 人工耳蜗植入已经成功地恢复了全球数十万人的听力。 然而，使用CI的语音理解性能仍然存在很大的变数。尽管存在这种可变性，但 结果，配置项通常用默认设置编程，只有几个特定于主题的编程 参数已调整。这可能是因为有太多可能的配置项设备设置组合 缺乏为个人CI用户实现最大利益的可用和数据驱动的指导。为 例如，大多数CI都是在所有可用电极都处于活动状态的情况下编程的，即使某些电极可能是 不适合传达语音信息的。先前的研究评估了选择性停用一些 这些电极中的一个改善了语音理解，但成效有限。然而，这些研究并没有 考虑如何在剩余的电极上表示语音信息，或最佳数量的 去激活电极。本研究的目标是使用计算驱动的语音模型 了解CI用户以指导搜索哪种有源电极组合可以产生最佳效果 针对特定患者的语音理解。 目标1是使用模型推荐的Active组合来量化语音理解和声音质量 将电极与临床护理标准设置进行比较，并与两种对照活性电极进行比较 组合。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以前的研究类似。受试者将拥有 定期使用1.5个月，配合每个实验活性电极的条件。通过实验实现性能 临床活性电极条件将采用重复测量设计进行比较。这是假设的 模型推荐的条件将导致比其他条件更好的语音理解 条件。目标2是将目标1中的模型驱动的建议转化为关于 要停用的CI电极的数量(以及可能是哪个)。受试者在实验和实验中的表现 利用AIM 1的临床活性电极条件，建立分层线性模型。这款车型将 将性能与实验活性电极设置与以下自变量相关联：数值 根据模型为受试者选择的有源电极的数目、有源电极的物理跨度、受试者的语音- 每个实验条件下的线索分辨率，以及三个人口统计学变量。据推测， 受试者的表现将与有源电极的数量和受试者的 语音线索分辨率，并与有源电极的物理跨度适度相关。 在当前提案的主持下获得的数据将是提供数据的更大规模研究的基础-- 为CI设备的最佳安装提供驱动指导。

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.