Noise induced hearing loss (NIHL) remains as a severe health problem worldwide. Existing noise metrics and modeling for evaluation of NIHL are limited on prediction of gradually developing NIHL (GDHL) caused by high-level occupational noise. In this study, we proposed two auditory fatigue based models, including equal velocity level (EVL) and complex velocity level (CVL), which combine the high-cycle fatigue theory with the mammalian auditory model, to predict GDHL. The mammalian auditory model is introduced by combining the transfer function of the external-middle ear and the triple-path nonlinear (TRNL) filter to obtain velocities of basilar membrane (BM) in cochlea. The high-cycle fatigue theory is based on the assumption that GDHL can be considered as a process of long-cycle mechanical fatigue failure of organ of Corti. Furthermore, a series of chinchilla experimental data are used to validate the effectiveness of the proposed fatigue models. The regression analysis results show that both proposed fatigue models have high corrections with four hearing loss indices. It indicates that the proposed models can accurately predict hearing loss in chinchilla. Results suggest that the CVL model is more accurate compared to the EVL model on prediction of the auditory risk of exposure to hazardous occupational noise.
Noise induced hearing loss (NIHL) is a serious problem that affects many people worldwide. According to the World Health Organization (WHO), exposure to excessive noise is the major avoidable cause of permanent hearing loss [
To estimate NIHL, various standards and regulations have been developed over years, for example, ISO 1999:2013 [
In recent years, the mammalian auditory model has been utilized to develop more advanced models for assessment and prediction of NIHL. Price [
Numerous auditory models (AMs) have been developed in the past decades [
For NIHL study, the key consideration for choosing an AM is how to accurately quantify the flow of acoustic power from the environment into the inner ear [
In functional auditory models, three families of auditory filters have been developed, including the rounded exponential (roex) family, the gammatone family (including gammachirp and all-pole and pole-zero variants), and the filter cascades (both all-pole and pole-zero variants) [
In material science, fatigue is a progressive and localized structural damage of a material caused by repeatedly applied loads. There are two kinds of material fatigues, low-cycle and high-cycle fatigues [
Assuming that the hearing loss intrinsically is a mechanical failure of the auditory system (i.e., basilar membrane or hair cells) [
In addition, instead of using the displacement of BM as the loads in [
In this study, two BM velocities based fatigue models, equivalent velocity level (EVL) and complex velocity level (CVL), are proposed to predict the GDHL caused by occupational noise. The auditory model combining the external-middle ear model and the TRNL filter is applied to obtain the BM velocities at different partitions of cochlea (i.e., Equivalent Rectangular Band (ERB)). Based on the stress against cycles to failure (
As shown in Figure
A schematic diagram of a model of auditory periphery, consisting of external ear, middle ear, and inner ear sections [
The primary function of the external ear and middle ear is gathering sound energy and conducting it into the inner ear. The middle ear acts as an impedance-matching device that extracts acoustic power from the stimulus and transmits it to cochlea [
The primary path for conducting environmental sound into inner ear is through the coupled motion of TM, ossicles, and stapes footplate. One can consider the “ossicular coupling” of sound to inner ear as a cascade of interdependent acoustical and mechanical processes, in which outputs of one stage act as inputs to the next stage. First, the pressure
(a) The gain of the external ear and (b) the transfer function of the middle ear of chinchilla.
One can assume that cochlea is a two-chambered, fluid-filled box with rigid side walls [
In this study, the TRNL filter introduced in [
Schematic diagram of the TRNL filter, in which the velocities of stapes in middle ear are passed through three parallel branches to obtain the velocities of BM.
Each individual bandpass function is a cascade of two or more gammatone filters [
Parameters of the TRNL algorithm used for the simulation of chinchilla’s inner ear [
800 Hz | 5500 Hz | 7250 Hz | 9750 Hz | 10000 Hz | 12000 Hz | 14000 Hz | |
---|---|---|---|---|---|---|---|
Linear | |||||||
Gammatone cascade | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
Low pass cascade | 7 | 7 | 7 | 7 | 7 | 7 | 7 |
|
750 | 5000 | 7400 | 9000 | 9000 | 11000 | 13000 |
|
450 | 3000 | 2500 | 3000 | 3500 | 5000 | 4000 |
|
750 | 6000 | 7400 | 9000 | 8800 | 12000 | 13500 |
Gain, |
500 | 190 | 3000 | 300 | 500 | 500 | 350 |
Nonlinear | |||||||
Gammatone cascade | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
Low pass cascade | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
|
730 | 5850 | 7800 | 9800 | 10000 | 12000 | 15000 |
|
350 | 1800 | 2275 | 1650 | 1800 | 2000 | 3200 |
|
730 | 5850 | 7800 | 9800 | 10000 | 12000 | 15000 |
Gain, |
850 | 3000 | 15000 | 9000 | 15000 | 22500 | 3000 |
Gain, |
0.03 | 0.04 | 0.06 | 0.05 | 0.06 | 0.07 | 0.045 |
Exponent, |
0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 |
Linear all-pass | |||||||
Gain, |
10 | 0.4 | 20 | 1 | 2 | 20 | 20 |
In this study, two fatigue models, the
The EVL model is proposed based on the
The CVL model is designed based on the Miner rule, which has been commonly used to predict high-cycle fatigue life. In the EVL model, the transition of adjacent stimulus is not accounted for because the loads in the
Rainflow matrix of BM velocities at the
As shown in Figure
A series of the animal experimental data were used for effectiveness validation of the proposed fatigue models. The animal data were provided by the research group at State University of New York at Plattsburgh and were used in published animal noise exposure experiments [
Detailed descriptions of the noise data and experimental protocols of animal studies are available in various publications [
OHC and IHC loss at different center frequencies of one-octave bands for different noise exposures.
Samples |
|
OHC | IHC | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.5 kHz | 1 kHz | 2 kHz | 4 kHz | 8 kHz | 16 kHz | 0.5 kHz | 1 kHz | 2 kHz | 4 kHz | 8 kHz | 16 kHz | ||
G44 | 100.6 | 20.8 | 38.1 | 67.9 | 67.5 | 62.7 | 42.7 | 1.6 | 5.6 | 22.8 | 21 | 22.7 | 6.9 |
G49 | 101 | 43.8 | 54.6 | 78.3 | 93.1 | 79.3 | 71.7 | 2.1 | 4.6 | 24.8 | 63.9 | 29.5 | 26.7 |
G50 | 100.5 | 11.8 | 21.7 | 15 | 12.7 | 28 | 25.6 | 0.7 | 2.6 | 2 | 4.7 | 12.3 | 10.7 |
G51 | 100.1 | 32.6 | 30.9 | 43.9 | 49.7 | 27 | 13.8 | 3.1 | 6.6 | 9.6 | 21.2 | 5 | 4 |
G52 | 101.7 | 39.1 | 40.8 | 64.7 | 66.3 | 41 | 20.5 | 3.4 | 6.8 | 18.8 | 35.6 | 7.6 | 6.9 |
G53 | 100.6 | 27.8 | 39.3 | 50.7 | 67.6 | 49.9 | 32.1 | 1 | 4 | 7.5 | 23.3 | 14.2 | 5 |
G54 | 100.6 | 21 | 23.7 | 60.4 | 69.9 | 35.1 | 17.8 | 1.1 | 1.8 | 22.1 | 22.7 | 11.9 | 9.4 |
G55 | 100.1 | 40.7 | 36.4 | 55.5 | 80.4 | 89.9 | 80.5 | 6.7 | 9.7 | 7.8 | 28.3 | 75.2 | 38.9 |
G60 | 100.2 | 35 | 34.2 | 54.1 | 72.4 | 47.5 | 29.5 | 2.1 | 2.4 | 22.6 | 32.1 | 11.3 | 12.3 |
G61 | 99.6 | 7.9 | 5.6 | 4.6 | 9.9 | 14.3 | 17.2 | 0.2 | 0.2 | 0.2 | 2.8 | 2.6 | 8.1 |
G63 | 99.6 | 35.6 | 50.1 | 65.8 | 75.6 | 42.3 | 36.1 | 5.1 | 17.3 | 26.2 | 26.2 | 10.2 | 11 |
G64 | 101.1 | 15.6 | 11.3 | 25.7 | 50.8 | 16 | 14.5 | 0.6 | 0.6 | 3.4 | 16.7 | 6.1 | 9.1 |
G65 | 99.7 | 24 | 16.7 | 25.1 | 71.3 | 90 | 46.7 | 1.5 | 1.8 | 1.4 | 15.4 | 61.6 | 22.9 |
G66 | 100.7 | 21.3 | 11.2 | 12.9 | 60.5 | 82.7 | 37.9 | 0.8 | 1 | 0.6 | 12.9 | 56.2 | 16.5 |
G68 | 99.7 | 22.8 | 22.4 | 30.5 | 70.4 | 89.9 | 58.9 | 1 | 1.6 | 7.7 | 23.8 | 52.3 | 15.9 |
G69 | 101 | 12.6 | 17.1 | 11.6 | 7.4 | 12.6 | 11.6 | 0.1 | 0.2 | 2.7 | 2.8 | 5.5 | 4.5 |
G70 | 100.7 | 23.4 | 19.9 | 52.2 | 89.6 | 46.1 | 38 | 0.7 | 0.9 | 8.8 | 50.7 | 18 | 16.4 |
G47 | 89.4 | 3.8 | 2.8 | 6.2 | 3.7 | 17 | 20.7 | 0.3 | 2.2 | 2.8 | 3.9 | 8.9 | 4 |
G48 | 91.7 | 5.9 | 4.6 | 2.5 | 5.8 | 9.7 | 15 | 0.4 | 0.4 | 1.5 | 2.7 | 4.6 | 9.5 |
G56 | 91.3 | 9 | 3.7 | 1.9 | 3.3 | 8.3 | 6.9 | 0.3 | 0.7 | 0.7 | 0.4 | 3.3 | 1.7 |
G57 | 94.2 | 28 | 24.2 | 15.7 | 10.2 | 13 | 16.7 | 0.4 | 0.6 | 2.5 | 3.2 | 5 | 11.6 |
G58 | 95.6 | 12.3 | 5.6 | 12.9 | 34.1 | 16.1 | 12.4 | 1 | 0.6 | 2.6 | 11 | 5.8 | 4.8 |
The distribution of BM velocities is obtained by the TRNL filter (as shown in Figure
Time-frequency presentations of BM velocities as the output of the TRNL filter, responding to (a) impulsive noise, (b) sweeping chirp noise at low frequency (400–500 Hz), and (c) sweeping chirp noise at high frequency (8000–12000 Hz). The labels of frequency axis indicate the different locations along BM, which refer to the partitions in cochlea.
BM velocity responding to the simulated impulse noise (the Dirac delta function) is shown in Figure
Furthermore, two chirp noise signals in different frequency bands were simulated and applied to the TRNL filter to validate the BM motion responding to different frequency components. Figures
Two experimental noise samples (i.e., G63 and G61) were used as inputs to validate the developed chinchilla auditory model, including the consecutive external-middle ear and the inner ear. Figure
Time-frequency presentations of the BM velocities obtained by the developed chinchilla auditory model, responding to two experimental noise samples: (a) G63 and (b) G61. The partial waveforms of G63 and G61 in 0.5 sec are shown in the top insert figures. The front views of the distributions of the BM velocities are shown in the bottom insert figures.
As shown in the front views in Figures
As shown in Figures
The correlations of the developed fatigue metrics (i.e.,
The linear regression analysis of the two developed fatigue metrics and four hearing loss indications (i.e., OHC loss, IHC loss, PTS, and TTS) at six one-octave frequency bands has been conducted using all 22 groups of experimental data. Figure
Scattering plots and fitting lines of pairs of the developed fatigue metrics,
Regression analysis at six one-octave bands centered at 0.5, 1, 2, 4, 8, and 16 kHz.
|
0.5 kHz | 1 kHz | 2 kHz | 4 kHz | 8 kHz | 16 kHz |
---|---|---|---|---|---|---|
|
0.22/0.08 | 0.18/0.04 | 0.48/ |
0.56/ |
0.28/0.01 | 0.18/0.05 |
|
0.26/0.01 | 0.26/0.01 | 0.69/ |
0.71/ |
0.48/ |
0.27/0.01 |
|
0.21/0.16 | 0.16/0.07 | 0.56/0.007 | 0.47/ |
0.14/0.08 | 0.24/0.12 |
|
0.25/0.06 | 0.20/0.06 | 0.54/0.01 | 0.56/ |
0.22/0.02 | 0.27/0.04 |
|
0.13/0.11 | 0.28/0.009 | 0.52/ |
0.58/ |
0.40/0.002 | 0.57/0.001 |
|
0.25/0.01 | 0.41/0.001 | 0.77/ |
0.71/ |
0.52/ |
0.65/0.001 |
|
0.39/0.003 | 0.53/ |
0.70/ |
0.71/ |
0.68/ |
0.58/ |
|
0.57/ |
0.65/ |
0.81/ |
0.76/ |
0.72/ |
0.79/ |
Moreover, the
Regression analysis at spectrum [1 kHz–8 kHz].
Pair |
|
|
---|---|---|
|
0.59 | 3.4 × 10−5 |
|
0.66 | 4.7 × 10−6 |
|
0.43 | 9.5 × 10−4 |
|
0.51 | 2.0 × 10−4 |
|
0.63 | 1.0 × 10−5 |
|
0.69 | 1.6 × 10−6 |
|
0.82 | 5.8 × 10−9 |
|
0.82 | 7.4 × 10−9 |
To further evaluate the effectiveness of the proposed fatigue metrics on NIHL prediction, hearing loss indices averaged by the one-octave bands centered at 1, 2, 4, and 8 kHz, including
Regression analysis of averaged
Regression analysis of averaged
The regression analysis results of the fatigue models and four effective hearing loss indices are summarized in Table
Moreover, one can also see that, in both Tables
In this study, we developed two fatigue models (i.e., the EVL and CVL models), which combined the high-cycle fatigue model with the mammalian auditory model, to predict GDHL. The high-cycle fatigue theory was used because GDHL caused by occupational noise can be considered as a long-time process of physical compression and stretching of organ of Corti. The mammalian auditory model was introduced by combining the TRNL filter with the transfer function of the external-middle ear to accurately characterize the vibration of BM. A series of animal noise exposure data were used to validate the effectiveness of the developed fatigue models. The regression analysis of fatigue models and four hearing loss indices was conducted. Results showed that both fatigue models have high correlations with animal hearing loss data. It indicates that the developed models can accurately predict the NIHL in chinchilla. In addition, the CVL model demonstrated higher correlations with four hearing loss indices than the EVL model. The CVL model would be more accurate on the evaluation of the auditory risk of exposure to hazardous occupational noise. In our future work, we will develop fatigue based models for prediction of auditory risk on human exposed to high-level occupational noise.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This research was supported in part by the US Department of Defense (DoD) (W81XWH-11-C-0031) and National Institute on Deafness and Other Communication Disorders (NIDCD) (R01 DC014549-01). The authors thank Wei Qiu at the State University of New York at Pittsburgh for providing chinchilla noise exposure study data.