The effects of noise‐induced hair cell lesions on cochlear electromechanical responses: A computational approach using a biophysical model

• Published in: International journal for numerical methods in biomedical engineering 2022-05, Vol.38 (5), p.e3582-n/a
• Main Authors: Saremi, Amin, Stenfelt, Stefan
• Format: Article
• Language: English
• Subjects: Auditory nerve; auditory periphery; Background noise; Basic Research; Cochlea; cochlear electromechanics; cochlear models; Coding; Compression; Computer applications; Frequency dependence; Hair; Hearing; Hearing loss; Lesions; Locking; Machine learning; Nelder–Mead optimization method; Neural coding; Noise; Noise prediction; noise-induced hearing loss; Nonlinear systems; Optimization; Optimization techniques; Signal processing; Simulation; Temporal variations; Thresholds; Tuning
• ISSN: 2040-7939; 2040-7947; 2040-7947
• DOI: 10.1002/cnm.3582

A biophysically inspired signal processing model of the human cochlea is deployed to simulate the effects of specific noise‐induced inner hair cell (IHC) and outer hair cell (OHC) lesions on hearing thresholds, cochlear compression, and the spectral and temporal features of the auditory nerve (AN) coding. The model predictions were evaluated by comparison with corresponding data from animal studies as well as human clinical observations. The hearing thresholds were simulated for specific OHC and IHC damages and the cochlear nonlinearity was assessed at 0.5 and 4 kHz. The tuning curves were estimated at 1 kHz and the contributions of the OHC and IHC pathologies to the tuning curve were distinguished by the model. Furthermore, the phase locking of AN spikes were simulated in quiet and in presence of noise. The model predicts that the phase locking drastically deteriorates in noise indicating the disturbing effect of background noise on the temporal coding in case of hearing impairment. Moreover, the paper presents an example wherein the model is inversely configured for diagnostic purposes using a machine learning optimization technique (Nelder–Mead method). Accordingly, the model finds a specific pattern of OHC lesions that gives the audiometric hearing loss measured in a group of noise‐induced hearing impaired humans. A biophysically‐inspired signal processing model of the human cochlea is deployed to simulate the effects of specific cellular lesions on the sound coding of the peripheral auditory system. A machine learning technique is developed based on Nelder‐Mead optimization method to enable the model to be used inversely for diagnostic purposes. Accordingly, the model finds a specific pattern of underlying cellular lesions that reproduces the clinically measured audiometric hearing loss in a group of noise‐induced hearing impaired humans.