Role of the Kurtosis Statistic in Evaluating Complex Noise Exposures for the Protection of Hearing

In Brief Objective: To highlight a selection of data that illustrate the need for better descriptors of complex industrial noise environments for use in the protection of hearing. Design: The data were derived using a chinchilla model. All noise exposures had the same total energy and the same spectrum; that is, they were equal energy exposures presented at an overall 100 dB(A) SPL that differed only in the scheduling of the exposure and the value of the kurtosis, β(t), a statistical metric. Hearing thresholds were determined before and after noise exposure using the auditory-evoked potential measured from the inferior colliculus in the brain stem. Cochlear damage was estimated from sensory-cell counts (cochleograms). Results: (1) For equivalent energy and spectra, exposure to a high-kurtosis, non-Gaussian noise produced substantially greater hearing and sensory-cell loss in the chinchilla model than a low-kurtosis, Gaussian noise. (2) β(t) computed on the amplitude distribution of the noise could clearly differentiate between the effects of Gaussian and non-Gaussian noise environments. (3) β(t) can order the extent of the trauma as determined by hearing thresholds and sensory-cell loss. Conclusions: The noise level in combination with the statistical properties of the noise quantified by β(t) clearly differentiate the effects between both continuous and interrupted and intermittent Gaussian and non-Gaussian noise environments. For the same energy and spectrum, the non-Gaussian environments are clearly the more hazardous. The use of both an energy and kurtosis metric can better predict the hazard of a high-level complex noise than the use of an energy metric alone (as is the current practice). These results point out the need for a new approach to the analysis and quantification of industrial noise for the purpose of hearing conservation practice. After many years of research, we are still unable to predict the noise-induced hearing loss that will be sustained by an individual in a given noise environment, thus limiting our ability to establish acceptable damage-risk criteria. A frequency-weighted energy metric in combination with the statistical metric, kurtosis, β(t), may provide necessary and possibly sufficient information to evaluate the potential of any industrial noise environment to cause hearing loss. Thresholds determined before and after noise exposure using the auditory-evoked potential measured from the inferior colliculus in the brain stem of the chinchilla, demonstrated that β(t) can refine our ability to predict the hazard to hearing associated with the temporal distribution of energy and provide a foundation for a more generalized and more accurate approach to the analysis and quantification of industrial noise for the purpose of hearing conservation practice.