Simulation of conductive hearing loss and its impact on distortion-product otoacoustic emissions using a hydrodynamic cochlea model.

Autor: Dalhoff, Ernst, Gummer, Anthony W., Zelle, Dennis
Předmět:
Zdroj: AIP Conference Proceedings; 2024, Vol. 3062 Issue 1, p1-7, 7p
Abstrakt: Distortion-product otoacoustic emissions (DPOAEs) are sound signals in the ear canal arising from nonlinear amplification of hydrodynamic oscillations in the cochlea. The analysis of the growth behavior of DPOAE pressure as function of stimulus level offers a quantitative assessment of the mechanical state of the cochlea. However, middle-ear dysfunction directly influences DPOAEs and their diagnostic accuracy. The present work simulates the impact of middle-ear dysfunction on DPOAEs using a hydrodynamic model of the human cochlea coupled to a middle-ear model for five frequencies from f2=1 to 4 kHz. DPOAEs in the ear canal are simulated by simultaneously solving the equations of motion representing the dynamics of the middle-ear and cochlea models in the time domain. Increasing the damping and stiffness of the annular ligament in the middle-ear model introduces conductive hearing loss (CHL) of various degrees. The changes in estimated distortion-product thresholds (EDPTs) and slopes of input-output (I/O) functions derived from the simulated DPOAE growth behavior are compared to changes in the middle-ear transfer functions (METFs). Pooled over all frequencies and degrees of CHL, the relative change of the METF amplitude at f2 exhibits a linear dependency on the relative change of EDPT level while the relative change of the METF amplitude at the distortion-product frequency fDP is linearly related to the relative change of the slope. The statistically significant linear relationships of the relative changes in EDPT level and slope of the I/O functions with relative changes in the METF amplitude indicate that conductive hearing loss can be objectively quantified from the DPOAE growth behavior. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index