Towards personalized auditory models: predicting individual sensorineural-hearing-loss profiles from recorded human auditory physiology

Abstract

Over the past decades, different types of auditory models have been developed to study the functioning of normal and impaired auditory processing. Several models can simulate frequency-dependent sensorineural hearing loss (SNHL), and can in this way be used to develop personalized audio-signal processing for hearing aids. However, to determine individualized SNHL profiles, we rely on indirect and non-invasive markers of cochlear and auditory-nerve (AN) damage. Our progressive knowledge of the functional aspects of different SNHL subtypes stresses the importance of incorporating them into the simulated SNHL profile, but has at the same time complicated the task of accomplishing this on the basis of non-invasive markers. In particular, different auditory evoked potential (AEP) types can show a different sensitivity to outer-hair-cell (OHC), inner-hair-cell (IHC) or AN damage, but it is not clear which AEP-derived metric is best suited to develop personalized auditory models. This study investigates how simulated and recorded AEPs can be used to derive individual AN- or OHC-damage patterns and personalize auditory processing models. First, we individualized the cochlear-model parameters using common methods of frequency-specific OHC-damage quantification, after which we simulated AEPs for different degrees of AN-damage. Using a classification technique, we determined the recorded AEP metric that best predicted the simulated individualized CS profiles. We cross-validated our method using the dataset at hand, but also applied the trained classifier to recorded AEPs from a new cohort to illustrate the generalisability of the method.