Evaluation of multivariate classification algorithms for hearing loss detection through a speech-in-noise test

Marta Lenatti¹, Edoardo M. Polo^2,3 Martina Paolini³, Maximiliano Mollura³, Marco Zanet¹, Riccardo Barbieri³, Alessia Paglialonga¹

¹Consiglio Nazionale delle Ricerche (CNR), Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni (IEIIT), Milan, Italy
²DIAG, Sapienza University of Rome, Rome, Italy
³Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Milan, Italy

Background: Online speech-in-noise screening tests are becoming increasingly popular as means to promote awareness and enable early identification of age-related hearing loss. To date, these tests are mainly based on the analysis of a single measure, that is the speech reception threshold (SRT). However, other features may provide significant predictors of hearing loss. The aim of this study is to address a hearing screening procedure that integrates a novel speech-in-noise test that can be used remotely in individuals of unknown language and artificial intelligence (AI) algorithms to analyze features extracted from the test.

Methods: In addition to the SRT, estimated using a newly developed staircase, our system extracted features such as percentage of correct responses, average reaction time, and test duration from 177 tested ears (including 68 ears with slight/mild or moderate hearing loss). These features were fed into a collection of AI algorithms including both explainable (XAI, e.g. Decision Tree) and conventional methods (e.g., Logistic Regression, Support Vector Machines) to train a multivariate classifier identifying ears with hearing loss.

Results: Our AI-based multivariate classifiers achieved better performance and sensitivity (e.g., Logistic Regression: AUC=0.88; sensitivity = 0.80; specificity = 0.77) when compared to a conventional univariate classifier based on SRT (cut-off: -8.87 dB SNR; AUC=0.82; sensitivity = 0.75; specificity = 0.81). According to XAI methods, in addition to SRT, other features like the number of correct responses and age were relevant in identifying slight/mild or higher degree of hearing loss.

Conclusion: The proposed hearing screening procedure showed good performance in terms of hearing loss detection. Ongoing research includes the implementation of an icon-based module to assess additional features, specifically risk factors for hearing loss (e.g., noise exposure, diabetes), that will be validated on a large population.

This study was partially supported by Capita Foundation (project WHISPER, Widespread Hearing Impairment Screening and PrEvention of Risk, 2020 Auditory Research Grant).