Development of an automatic classifier for the prediction of hearing impairment from industrial noise exposure

Abstract

The ISO-1999 [(2013). International Organization for Standardization, Geneva, Switzerland] standard is the most commonly used approach for estimating noise-induced hearing trauma. However, its insensitivity to noise characteristics limits its practical application. In this study, an automatic classification method using the support vector machine (SVM) was developed to predict hearing impairment in workers exposed to both Gaussian (G) and non-Gaussian (non-G) industrial noises. A recently collected human database (N = 2,110) from industrial workers in China was used in the present study. A statistical metric, kurtosis, was used to characterize the industrial noise. In addition to using all the data as one group, the data were also broken down into the following four subgroups based on the level of kurtosis: G/quasi-G, low-kurtosis, middle-kurtosis, and high-kurtosis groups. The performance of the ISO-1999 and the SVM models was compared over these five groups. The results showed that: (1) The performance of the SVM model significantly outperformed the ISO-1999 model in all five groups. (2) The ISO-1999 model could not properly predict hearing impairment for the high-kurtosis group. Moreover, the ISO-1999 model is likely to underestimate hearing impairment caused by both G and non-G noise exposures. (3) The SVM model is a potential tool to predict hearing impairment caused by diverse noise exposures.