Objective comparison of auditory profiles using manifold learning and intrinsic measures

TL;DR

This study systematically compares various auditory profiling frameworks using manifold learning and intrinsic measures, revealing how clustering methods and profile numbers influence the quality of auditory profiles, and identifying the Hearing4All framework as particularly effective.

Contribution

It provides a systematic comparison of auditory profiling frameworks using intrinsic measures and manifold learning, highlighting the impact of clustering choices and identifying a promising new framework.

Findings

Bisgaard profiles showed strongest clustering performance among audiogram-based methods.

Hearing4All profiles achieved high clustering quality with 13 classes.

Manifold learning effectively compares and evaluates auditory profiling frameworks.

Abstract

Assigning individuals with hearing impairment to auditory profiles can support a better understanding of the causes and consequences of hearing loss and facilitate profile-based hearing-aid fitting. However, the factors influencing auditory profile generation remain insufficiently understood, and existing profiling frameworks have rarely been compared systematically. This study therefore investigated the impact of two key factors - the clustering method and the number of profiles - on auditory profile generation. In addition, eight established auditory profiling frameworks were systematically reviewed and compared using intrinsic statistical measures and manifold learning techniques. Frameworks were evaluated with respect to internal consistency (i.e., grouping similar individuals) and cluster separation (i.e., clear differentiation between groups). To ensure comparability, all analyses were conducted on a common open-access dataset, the extended Oldenburg Hearing Health Record (OHHR), comprising 1,127 participants (mean age = 67.2 years, SD = 12.0). Results showed that both the clustering method and the chosen number of profiles substantially influenced the resulting auditory profiles. Among purely audiogram-based approaches, the Bisgaard auditory profiles demonstrated the strongest clustering performance, whereas audiometric phenotypes performed worst. Among frameworks incorporating supra-threshold information in addition to the audiogram, the Hearing4All auditory profiles were advantageous, combining a near-optimal number of profile classes (N = 13) with high clustering quality, as indicated by a low Davies-Bouldin index. In conclusion, manifold learning and intrinsic measures enable systematic comparison of auditory profiling frameworks and identify the Hearing4All auditory profile as a promising approach for future research.