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Energy-independent factors influencing noise-induced hearing loss in the chinchilla model.
J Acoust Soc Am 2001 Dec; 110(6):3163-3168
The effects on hearing and the sensory cell population of four continuous, non-Gaussian noise exposures each having an A-weighted L(eq)=100 dB SPL were compared to the effects of an energy-equivalent Gaussian noise. The non-Gaussian noise conditions were characterized by the statistical metric, kurtosis (beta), computed on the unfiltered, beta(t), and the filtered, beta(f), time-domain signals. The chinchilla (n=58) was used as the animal model. Hearing thresholds were estimated using auditory-evoked potentials (AEP) recorded from the inferior colliculus and sensory cell populations were obtained from surface preparation histology. Despite equivalent exposure energies, the four non-Gaussian conditions produced considerably greater hearing and sensory cell loss than did the Gaussian condition. The magnitude of this excess trauma produced by the non-Gaussian noise was dependent on the frequency content, but not on the average energy content of the impacts which gave the noise its non-Gaussian character. These results indicate that beta(t) is an appropriate index of the increased hazard of exposure to non-Gaussian noises and that beta(f) may be useful in the prediction of the place-specific additional outer hair cell loss produced by non-Gaussian exposures. The results also suggest that energy-based metrics, while necessary for the prediction of noise-induced hearing loss, are not sufficient.
Noise-exposure; Hearing-loss; Etiology; Exposure-limits; Risk-factors; Animals; Laboratory-animals; Environmental-exposure; Statistical-analysis; Workers; Auditory-system; Sound
Roger P. Hamernik, Auditory Research Laboratory, State University of New York, Plattsburgh 12901
Issue of Publication
Journal of the Acoustical Society of America
Plattsburgh State University, New York
Page last reviewed: May 5, 2020
Content source: National Institute for Occupational Safety and Health Education and Information Division