A Graph-Based Method for Optimal Active Electrode Selection in Cochlear Implants

Abstract

The cochlear implant (CI) is a neural prosthetic that is the standard-of-care treatment for severe-toprofound hearing loss. CIs consist of an electrode array inserted into the cochlea that electrically stimulates auditory nerve fibers to induce the sensation of hearing. Competing stimuli occur when multiple electrodes stimulate the same neural pathways. This is known to negatively impact hearing outcomes. Previous research has shown that image-processing techniques can be used to analyze the CI position in CT scans to estimate the degree of competition between electrodes based on the CI user's unique anatomy and electrode placement. The resulting data permits an algorithm or expert to select a subset of electrodes to keep active to alleviate competition. Expert selection of electrodes using this data has been shown in clinical studies to lead to significantly improved hearing outcomes for CI users. Currently, we aim to translate these techniques to a system designed for worldwide clinical use, which mandates that the selection of active electrodes be automated by robust algorithms. Previously proposed techniques produce optimal plans with only 48% success rate. In this work, we propose a new graph-based approach. We design a graph with nodes that represent electrodes and edge weights that encode competition between electrode pairs. We then find an optimal path through this graph to determine the active electrode set. Our method produces results judged by an expert to be optimal in over 95% of cases. This technique could facilitate widespread clinical translation of image-guided cochlear implant programming methods.

Activation region overlap visualization for image-guided cochlear implant programming

“…These results indicate that the ARO image outperforms the DVF curves in repeatability, acceptability, and time taken to generate plans. Other works have aimed to develop automatic methods for selecting deactivation plans (Bratu et al 2020). In a future study, we will evaluate the use of this visualization technique to review automatic plans created using such methods.…”