Real-Time Data-Driven Approach for Prediction and Correction of Electrode Array Trajectory in Cochlear Implantation

Abstract

Cochlear implants provide hearing perception to people with severe to profound hearing loss. The electrode array (EA) inserted during the surgery directly stimulates the hearing nerve, bypassing the acoustic hearing system. The complications during the EA insertion in the inner ear may cause trauma leading to infection, residual hearing loss, and poor speech perception. This work aims to reduce the trauma induced during electrode array insertion process by carefully designing a sensing method, an actuation system, and data-driven control strategy to guide electrode array in scala tympani. Due to limited intra-operative feedback during the insertion process, complex bipolar electrical impedance is used as a sensing element to guide EA in real time. An automated actuation system with three degrees of freedom was used along with a complex impedance meter to record impedance of consecutive electrodes. Prediction of EA direction (medial, middle, and lateral) was carried out by an ensemble of random forest, shallow neural network, and k-nearest neighbour in an offline setting with an accuracy of 86.86%. The trained ensemble was then utilized in vitro for prediction and correction of EA direction in real time in the straight path with an accuracy of 80%. Such a real-time system also has application in other electrode implants and needle and catheter insertion guidance.

Keywords

• complex bipolar impedance
• electrode array trajectory
• classifier-in-a-loop
• machine intelligence