Classification of Hearing Aids Into Feature Profiles Using Hierarchical Latent Class Analysis Applied to a Large Dataset of Hearing Aids

Abstract

Objectives: We developed a framework for objectively comparing hearing aids, independent of brand, type, or product family. This was done using a large dataset of commercially available hearing aids. To achieve this, we investigated which hearing aid features are suitable for comparison, and are also relevant for the rehabilitation of hearing impairment. To compare hearing aids objectively, we distinguished populations of hearing aids based on a set of key hearing aid features. Finally, we describe these hearing aid subpopulations so that these could potentially be used as a supporting tool for the selection of an appropriate hearing aid.

Design: In this study, we used technical (meta-)data from 3911 hearing aids (available on the Dutch market in March 2018). The dataset contained about 50 of the most important characteristics of a hearing aid. After cleaning and handling the data via a well-defined knowledge discovery in database procedure, a total 3083 hearing aids were included. Subsequently, a set of well-defined key hearing aid features were used as input for further analysis. The data were split into an in-the-ear style hearing aid subset and a behind-the-ear style subset, for separate analyses. The knowledge discovery in databases procedure was also used as an objective guiding tool for applying an exploratory cluster analysis to expose subpopulations of hearing aids within the dataset. The latter was done using Latent Class Tree Analysis, which is an extension to the better-known Latent Class Analysis clustering method: with the important addition of a hierarchical structure.

Results: A total of 10 hearing aid features were identified as relevant for audiological rehabilitation: compression, sound processing, noise reduction (NR), expansion, wind NR, impulse (noise) reduction, active feedback management, directionality, NR environments, and ear-to-ear communication. These features had the greatest impact on results yielded by the Latent Class Tree cluster analysis. At the first level in the hierarchical cluster model, the two subpopulations of hearing aids could be divided into 3 main branches, mainly distinguishable by the overall availability or technology level of hearing aid features. Higher-level results of the cluster analysis yielded a set of mutually exclusive hearing aid populations, called modalities. In total, nine behind-the-ear and seven in-the-ear modalities were found. These modalities were characterized by particular profiles of (complex) interplay between the selected key features. A technical comparison of features (e.g., implementation) is beyond the scope of this research.

Conclusions: Combining a large dataset of hearing aids with a probabilistic hierarchical clustering method enables analysis of hearing aid characteristics which extends beyond product families and manufacturers. Furthermore, this study found that the resulting hearing aid modalities can be thought of as a generic alternative to the manufacturer-dependent proprietary "concepts," and could potentially aid the selection of an appropriate hearing aid for technical rehabilitation. This study is in line with a growing need for justification of hearing aid selection and the increasing demand for evidence-based practice.

Fig. 1.

Graphical representation of the final LCT results: (A) the BTE subset, and (B) the ITE subset. The top node for each model is the starting point for the model at level 0, the modalities are at the lowest level at the end of the tree. The underlined numbers inside the nodes correspond to the number of allotted hearing aids. The allocation of hearing aids to a class is based on probabilities. Therefore, slight deviations could occur between the sum of child nodes and the parent node. BTE indicates behind-the-ear; ITE, in-the-ear; LCT, Latent Class Trees.

Fig. 2.

Example showing the dependency of hearing aid feature profiles on prior “parent” models; each LCA model in the tree is dependent on (all) prior LCA model results up to the lowest level at the end of the tree. The bottom profile plots show the formation of two different profiles within the first BTE branch. In this example, a “parent” model at level 3 (striped dark grey line; highlighted in the top left panel) splits into two “child” nodes (schematically shown in the top right panel). As there were no further splits from the models at the bottom panels onward, the results at this level were considered BTE modality E and F. Mean hearing aid feature data were rescaled between 0 and 1 to enable a straightforward comparison between different scaled variables. Features were ordered according the three domains: signal processing, comfort, and adaptation. Whiskers show a 95% confidence interval of the specific feature. The lines between the points do not refer to a dependency between adjacent features, but were included to interpret and compare the feature profiles between modalities. Labels x axis: compression (C), sound processing (SP), noise reduction (NR), expansion (Ex), wind noise reduction (WNR), impulse (noise) reduction (IR), active feedback management (FBM), directionality (Dir), noise reduction environments (NRe), ear-to-ear communication (ETE). BTE indicates behind-the-ear; LCA, Latent Class Analysis.

Fig. 3.

Profiles plots primary node, for the BTE subset (upper panel) and the ITE subset (lower panel): profile 1 (dark grey), profile 2 (light grey), profile 3 (blue). For axis configuration, see also Figure 2. BTE indicates behind-the-ear; ITE, in-the-ear.

Fig. 4.

Profile plots of nine final BTE modalities A to I: solid lines represent mean features measures for the specific modality, while dashed lines show mean feature measure of all devices in the dataset. For axis configuration, see also Figure 2. BTE indicates behind-the-ear.\

Fig. 5.

Profile plots of seven final ITE modalities a to g: solid lines represent mean features measures for the specific modality, while dashed lines show mean feature measure of all devices in the dataset. For axis configuration, see also Figure 2. ITE indicates in-the-ear.

Fig. 6.

Top of the table shows the number of hearing aids included in the analysis, for each year of introduction and hearing aid style type. The three graphs show rescaled mean hearing aid feature potential per domain (y axis) for grouped hearing aids (BTE: light grey squares; ITE: dark grey dots), plotted against the year of introduction (x axis) from 2009 up to and including 2018. The x axes of all three graphs share the same grouped hearing aids per year of introduction, and a linear model was fitted on each of the domain-specific data. The top graph shows the domain signal processing. The middle graph shows the domain comfort, and the bottom graph shows the domain adaptation. BTE indicates behind-the-ear; ITE, in-the-ear.

Fig. 7.

Distribution of the BTE subset (upper panels) and ITE subset (lower panels), based on a selection of eight hearing aid brands (in random order). The percentages in (A) represent the distribution for each modality (row), while the percentages in (B) represent the distribution for each brand (column). The surface tone gives an indication of the underlying number. BTE indicates behind-the-ear; ITE, in-the-ear.