arXiv subjects

Lena Schell-Majoor

Publications and source records attributed to Lena Schell-Majoor.

4 recordsLinked to original sources

arXiv2026

The Virtual Hearing Clinic (VHC)- a modular online platform for hearing research and hearing health care

Objective: The aim is to introduce the concept of the Virtual Hearing Clinic (VHC), give an overview of the current status and exemplify its feasibility with data from a diagnostics module obtaining hearing thresholds. Design: The architecture of the VHC is described and an overview of functional modules that have been developed and tested in respective studies is given. As a functional example data from an experiment is presented. Hearing thresholds were obtained from 20 subjects with hearing loss with the VHC using the Graded Response Bracketing (GraBr) procedure and compared to reference thresholds obtained with a clinical audiometer. Results: Median VHC-based hearing thresholds over all frequencies did not differ significantly from the reference with values of 57.4 dB SPL (VHC) and 55.5 dB SPL (reference). The shape of the frequency-dependent thresholds was also found to be very similar for 250 Hz to 4 kHz. Results for 6 kHz showed larger differences. Conclusion: The VHC is suitable as a mobile hearing health application and to collect data for audiological research. By offering flexibility in time and location it can lower barriers for hearing health care and enable collecting large datasets that are needed for the advancement of data-driven audiology.

physics.med-ph↗

arXiv2026

Objective comparison of auditory profiles using manifold learning and intrinsic measures

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.

physics.med-ph↗

arXiv2025

Standard audiogram classification from loudness scaling data using unsupervised, supervised, and explainable machine learning techniques

To address the calibration and procedural challenges inherent in remote audiogram assessment for rehabilitative audiology, this study investigated whether calibration-independent adaptive categorical loudness scaling (ACALOS) data can be used to approximate individual audiograms by classifying listeners into standard Bisgaard audiogram types using machine learning. Three classes of machine learning approaches - unsupervised, supervised, and explainable - were evaluated. Principal component analysis (PCA) was performed to extract the first two principal components, which together explained more than 50 percent of the variance. Seven supervised multi-class classifiers were trained and compared, alongside unsupervised and explainable methods. Model development and evaluation used a large auditory reference database containing ACALOS data (N = 847). The PCA factor map showed substantial overlap between listeners, indicating that cleanly separating participants into six Bisgaard classes based solely on their loudness patterns is challenging. Nevertheless, the models demonstrated reasonable classification performance, with logistic regression achieving the highest accuracy among supervised approaches. These findings demonstrate that machine learning models can predict standard Bisgaard audiogram types, within certain limits, from calibration-independent loudness perception data, supporting potential applications in remote or resource-limited settings without requiring a traditional audiogram.

cs.SD↗

arXiv2025

Discrimination loss vs. SRT: A model-based approach towards harmonizing speech test interpretations

Objective: Speech tests aim to estimate discrimination loss or speech recognition threshold (SRT). This paper investigates the potential to estimate SRTs from clinical data that target at characterizing the discrimination loss. Knowledge about the relationship between the speech test outcome variables--conceptually linked via the psychometric function--is important towards integration of data from different databases. Design: Depending on the available data, different SRT estimation procedures were compared and evaluated. A novel, model-based SRT estimation procedure was proposed that deals with incomplete patient data. Interpretations of supra-threshold deficits were assessed for the two interpretation modes. Study sample: Data for 27009 patients with Freiburg monosyllabic speech test (FMST) and audiogram (AG) results from the same day were included in the retrospective analysis. Results: The model-based SRT estimation procedure provided accurate SRTs, but with large deviations in the estimated slope. Supra-threshold hearing loss components differed between the two interpretation modes. Conclusions: The model-based procedure can be used for SRT estimation, and its properties relate to data availability for individual patients. All SRT procedures are influenced by the uncertainty of the word recognition scores. In the future, the proposed approach can be used to assess additional differences between speech tests.

cs.SD↗