Automatic analysis of cochlear response using electrocochleography signals during cochlear implant surgery

Cochlear implants (CIs) provide an opportunity for the hearing impaired to perceive sound through electrical stimulation of the hearing (cochlear) nerve. However, there is a high risk of losing a patient's natural hearing during CI surgery, which has been shown to reduce speech perception in noisy environments as well as music appreciation. This is a major barrier to the adoption of CIs by the hearing impaired. Electrocochleography (ECochG) has been used to detect intra-operative trauma that may lead to loss of natural hearing. There is early evidence that ECochG can enable early intervention to save natural hearing of the patient. However, detection of trauma by observing changes in the ECochG response is typically carried out by a human expert. Here, we discuss a method of automating the analysis of cochlear responses during CI surgery. We establish, using historical patient data, that the proposed method is highly accurate (similar to 94% and similar to 95% for sensitivity and specificity respectively) when compared to a human expert. The automation of real-time cochlear response analysis is expected to improve the scalability of ECochG and improve patient safety.

Automated summary

Cochlear implants (CIs) offer the hearing impaired a chance to perceive sound through electrical stimulation of the cochlear nerve. However, CI surgery carries a high risk of losing natural hearing, which can impact speech perception and music appreciation. Electrocochleography (ECochG) has been used to detect trauma during surgery and potentially save natural hearing. This study discusses an automated method for analyzing cochlear responses during CI surgeries, demonstrating high accuracy compared to human experts and the potential to improve ECochG scalability and patient safety.