The effects of the amplitude distribution of equal energy exposures on noise-induced hearing loss: The kurtosis metric

Abstract

Seventeen groups of chinchillas with 11 to 16 animals/group (sigmaN = 207) were exposed for 5 days to either a Gaussian (G) noise or 1 of 16 different non-Gaussian (non-G) noises at 100 dB(A) SPL. All exposures had the same total energy and approximately the same flat spectrum but their statistical properties were varied to yield a series of exposure conditions that varied across a continuum from G through various non-G conditions to pure impact noise exposures. The non-G character of the noise was produced by inserting high level transients (impacts or noise bursts) into the otherwise G noise. The peak SPL of the transients, their bandwidth, and the intertransient intervals were varied, as was the rms level of the G noise. The statistical metric, kurtosis (beta), computed on the unfiltered noise beta(t), was varied 3 < or = beta(t) < or = 105. Brainstem auditory evoked responses were used to estimate hearing thresholds and surface preparation histology was used to determine sensory cell loss. Trauma, as measured by asymptotic and permanent threshold shifts (ATS, PTS) and by sensory cell loss, was greater for all of the non-G exposure conditions. Permanent effects of the exposures increased as beta(t) increased and reached an asymptote at beta(t) approximately 40. For beta(t) > 40 varying the interval or peak histograms did not alter the level of trauma, suggesting that, in the chinchilla model, for beta(t) > 40 an energy metric may be effective in evaluating the potential of non-G noise environments to produce hearing loss. Reducing the probability of a transient occurring could reduce the permanent effects of the non-G exposures. These results lend support to those standards documents that use an energy metric for gauging the hazard of exposure but only after applying a "correction factor" when high level transients are present. Computing beta on the filtered noise signal [beta(f)] provides a frequency specific metric for the non-G noises that is correlated with the additional frequency specific outer hair cell loss produced by the non-G noise. The data from the abundant and varied exposure conditions show that the kurtosis of the amplitude distribution of a noise environment is an important variable in determining the hazards to hearing posed by non-Gaussian noise environments.

Use of the kurtosis statistic in an evaluation of the effects of noise and solvent exposures on the hearing thresholds of workers: An exploratory study

“…Jobs involving maintenance work, metalwork, and power tools, such as impact wrenches and nail guns, provide examples of complex noise environments. Typically, as the temporal distribution of these noise environments is not normal or Gaussian (G), they may be described as "complex non-Gaussian (non-G)" noise (Hamernik et al, 2003). Non-G noise is very common in factories where it is comprised of a background G noise along with embedded high-level transients (impacts or noise bursts).…”

The value of a kurtosis metric in estimating the hazard to hearing of complex industrial noise exposures

“…microphone. For all exposures a mean value of b(t) was obtained by averaging b(t) computed on consecutive 40 s windows (Hamernik et al, 2003b) over a 5.5 min sample of the exposure waveform using MATLAB (MathWorks) software. Similarly, b(f) was obtained by first filtering the acoustic signal over consecutive octave bands centered on the AEP test frequencies and computing the value of b(f) on the temporal waveform of the filtered signals.…”

“…The experiments described below were designed to test this hypothesis. However, the experimental design also allowed for (a) a replication or reinforcement of earlier results that showed increased trauma produced by nonG exposures relative to equivalent energy G exposures (Hamernik et al, 2003b;Hamernik and Qiu, 2001;Lei et al, 1994); (b) an evaluation of the equal energy principle as it applies to interrupted/intermittent noise exposures Hamernik et al, 2007); and (c) the effect of toughening/conditioning (Miller et al, 1963;Clark et al, 1987;Canlon et al, 1988;Hamernik et al, 2003aHamernik et al, , 1994Hamernik and Ahroon, 1999) on the resultant hearing and sensory cell loss.…”

“…The kurtosis [b(t)] can be computed on the amplitude distribution of the temporal waveform of the noise presented to the subject or by filtering the waveform, a frequency specific kurtosis [b(f)] can be computed on the resultant time-domain signal. Experimental animal model data using the chinchilla have shown that for a fixed energy of exposure, hearing and sensory cell loss increase as b(t) of the amplitude distribution of the noise stimulus increases (Hamernik and Qiu, 2001;Hamernik et al, 2003b), while b(f) has been shown to be related to the frequency specific sensory cell loss caused by a high b(t) noise exposure (Hamernik et al, 2003b). These and other experimental results (Zhao et al, 2010) have suggested that the kurtosis metric in combination with an energy metric may be a better index of the potential of a noise exposure to cause hearing loss than is an energy based metric alone.…”

Development of a digital noise exposure system for research on noise induced hearing loss

“…Noise can be classified into Gaussian continuous noise (i.e., stead state noise), high-level transient noise (i.e., impulsive noise including impulse noise and impact noise), and complex noise (i.e., a non-Gaussian noise consisting of high-level transients noise mixed in a Gaussian noise) [6][7][8]. All types of noises could cause hearing loss at high noise level [4].…”