A Machine Learning Based Clustering Protocol for Determining Hearing Aid Initial Configurations from Pure-Tone Audiograms.

Abstract

Of the nearly 35 million people in the USA who are hearing impaired, only an estimated 25% use hearing aids (HA). A good number of HAs are prescribed but not used partially because of the time to convergence for best operation between the audiologist and user. To improve HA retention, it is suggested that a machine learning (ML) protocol could be established which improves initial HA configurations given a user's pure-tone audiogram. This study examines a ML clustering method to predict the best initial HA fitting from a corpus of over 90,000 audiogram-fitting pairs collected from hearing centers throughout the USA. We first examine the final HA comfort targets to determine a limited number of preset configurations using several multi-dimensional clustering methods (Birch, Ward, and k-means). The goal is to reduce the amount of adjustments between the centroid, selected as a fitting configuration to represent the cluster, and the final HA configurations. This may be used to reduce the adjustment cycles for HAs or as preset starting configurations for personal sound amplification products (PSAPs). Using various classification methods, audiograms are mapped to a limited number of potential preset configurations. Finally, the average adjustment between the preset fitting targets and the final fitting targets is examined.