Clinical Interventions in Aging (Jun 2020)
Detection of Age-Related Hearing Losses (ARHL) via Transient-Evoked Otoacoustic Emissions
Abstract
Giovanna Zimatore,1,2 Marta Cavagnaro,3 Piotr H Skarzynski,4– 6 Anna R Fetoni,7 Stavros Hatzopoulos8 1Department of Theoretical and Applied Sciences, eCampus University, Novedrate, CO 22060, Italy; 2Department of Movement, Human and Health Sciences, University of Rome Foro Italico, Rome, Italy; 3Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, Rome, Italy; 4World Hearing Center, Warsaw, Poland; 5Department of Heart Failure and Cardiac Rehabilitation, Medical University of Warsaw, Warsaw, Poland; 6Institute of Sensory Organs, Kajetany, Poland; 7Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome 00168, Italy; 8Clinic of Audiology and ENT, University of Ferrara, Ferrara, ItalyCorrespondence: Giovanna ZimatoreDepartment of Theoretical and Applied Sciences, eCampus University Tel +39 338 959 4393Email [email protected]: The objective of the study was to identify subjects presenting hearing deficits, specifically age-related hearing losses (ARHL), via objective assessment methodologies.Materials and Methods: Initially, 259 subjects (165 men, 94 women) were enrolled in the study. After the application of inclusion criteria, the final number was reduced to 88 subjects (49.8 ± 19.1 ys) subdivided into 64 normal and 83 ARHL cases. The subjects were assessed with traditional audiometry tests and with transiently evoked otoacoustic emissions (TEOAEs). Since each ear has its own acoustic signature, the TEOAE analyses were conducted in terms of ears and not subjects. The TEOAE data were processed by traditional and recurrence quantification analyses, leading to the estimation of the WWR (whole waveform reproducibility) and the new RAD2D (2-dimensional radius) parameters. A plot of WWR vs RAD2D was used to optimize the classification of the cases presenting ARHL.Results: By using a WWR value of 70% as a classifier, the sensitivity of TEOAEs was estimated as 75.9% and the specificity as 89.1%. By using the RAD2D parameter (with a cut-off value of 1.78), a sensitivity value of 80.7% and a specificity value of 71.9% were obtained. When both parameters were used, a sensitivity value of 85.5% and a specificity value of 92.2% were estimated. In the latter classification paradigm, the number of false negatives decreased from 20 to 12 out of 83 ears (14%).Conclusion: In adult hearing screening assessments, the proposed method optimizes the identification of subjects with a hearing impairment correlated to the presence of age-related hearing loss.Keywords: recurrence quantification analysis, early detection, otoacoustic emissions, TEOAEs, determinism, presbycusis
