ArticleTrends in hearing
Objective Comparison of Auditory Profiles Using Manifold Learning and Intrinsic Measures.
Article in Trends in hearing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 achieved the lowest normalized Davies-Bouldin (DB) score, while the BEAR auditory profiles performed better on the other intrinsic measures. In conclusion, separability should be considered a primary criterion in auditory profile generation, as it directly determines how meaningfully different profiles can be distinguished in practice. Manifold learning and intrinsic measures enable systematic comparisons of auditory profiling frameworks and identify the Hearing4All auditory profile as a promising approach for future research.
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