How Does Deep Neural Network-Based Noise Reduction in Hearing Aids Impact Cochlear Implant Candidacy?

Background/objectives: Adult hearing-impaired patients qualifying for cochlear implants typically exhibit less than 60% sentence recognition under the best hearing aid conditions, either in quiet or noisy environments, with speech and noise presented through a single speaker. This study examines the influence of deep neural network-based (DNN-based) noise reduction on cochlear implant evaluation.

Methods: Speech perception was assessed using AzBio sentences in both quiet and noisy conditions (multi-talker babble) at 5 and 10 dB signal-to-noise ratios (SNRs) through one loudspeaker.

Pathophysiology of Facial Nerve Stimulation and Its Implications for Electrical Stimulation in Cochlear Implants.

Objectives: A small number of cochlear implant (CI) users experience facial nerve stimulation (FNS), which can manifest as facial twitching. In some patients, this can be resolved by adjusting the electrical stimulation parameters. However, for others, facial stimulation can significantly impair CI outcomes or even prevent its use.