Dual-stage algorithm to identify channels with poor electrode-to-neuron interface in cochlear implant users

Stefano Cosentino, Lindsay De Vries, Rachel Scheperle, Julie Bierer, R. Carlyon
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引用次数: 2

Abstract

Users of cochlear implants rely on a number of electrodes to perceive acoustic information. The extent to which their hearing is restored depends on a number of factors including the electrode-to-neuron interface. We describe an approach to detect instances of poor-performing channels based on physiological data known as electrically evoked compound action potentials (ECAPs). The proposed approach - termed Panoramic ECAP ("PECAP") - combines nonlinear optimization stages with different constraints to recover neural activation patterns for all electrodes. Data were obtained from nine cochlear implant subjects and used to run the PECAP tool to identify possible instances of poor-performing channels. Data from one subject revealed a shifted peak ("dead region").
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人工耳蜗使用者电极-神经元界面差通道的双阶段识别算法
人工耳蜗的使用者依靠许多电极来感知声音信息。他们的听力恢复程度取决于许多因素,包括电极-神经元界面。我们描述了一种基于被称为电诱发复合动作电位(ECAPs)的生理数据来检测表现不佳的通道实例的方法。所提出的方法被称为全景ECAP(“PECAP”),它结合了具有不同约束条件的非线性优化阶段,以恢复所有电极的神经激活模式。数据来自9名人工耳蜗受试者,并用于运行PECAP工具来识别可能表现不佳的通道。一名受试者的数据显示了一个移位的峰值(“死区”)。
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