Estimation of Occupational Noise-Induced Hearing Loss Using Kurtosis-Adjusted Noise Exposure Levels

A Noise-Induced Hearing Loss Prediction Model Based on Asymmetric Convolution for Workers Exposed to Complex Industrial Noise

OBJECTIVES

Current approaches for evaluating noise-induced hearing loss (NIHL), such as the International Standards Organization 1999 (ISO) 1999 prediction model, rely mainly on noise energy and exposure time, thus ignoring the intricate time-frequency characteristics of noise, which also play an important role in NIHL evaluation. In this study, an innovative NIHL prediction model based on temporal and spectral feature extraction using an asymmetric convolution algorithm is proposed.

DESIGN

Personal data and individual occupational noise records from 2214 workers across 23 factories in Zhejiang Province, China, were used in this study. In addition to traditional metrics like noise energy and exposure duration, the importance of time-frequency features in NIHL assessment was also emphasized. To capture these features, operations such as random sampling, windowing, short-time Fourier transform, and splicing were performed to create time-frequency spectrograms from noise recordings. Two asymmetric convolution kernels then were used to extract these critical features. These features, combined with personal information (e.g., age, length of service) in various configurations, were used as model inputs. The optimal network structure was selected based on the area under the curve (AUC) from 10-fold cross-validation, alongside the Wilcoxon signed ranks test. The proposed model was compared with the support vector machine (SVM) and ISO 1999 models, and the superiority of the new approach was verified by ablation experiments.

RESULTS

The proposed model had an AUC of 0.7768 ± 0.0223 (mean ± SD), outperforming both the SVM model (AUC: 0.7504 ± 0.0273) and the ISO 1999 model (AUC: 0.5094 ± 0.0071). Wilcoxon signed ranks tests confirmed the significant improvement of the proposed model ( p = 0.0025 compared with ISO 1999, and p = 0.00142 compared with SVM).

CONCLUSIONS

This study introduced a new NIHL prediction method that provides deeper insights into industrial noise exposure data. The results demonstrated the superior performance of the new model over ISO 1999 and SVM models. By combining time-frequency features and personal information, the proposed approach bridged the gap between conventional noise assessment and machine learning-based methods, effectively improving the ability to protect workers' hearing.

Recent Advances in Preparation and Structure of Polyurethane Porous Materials for Sound Absorbing Application

Various acoustic materials are developed to resolve noise pollution problem in many industries. Especially, materials with porous structure are broadly used to absorb sound energy in civil construction and transportation area. Polyurethane (PU) porous materials possess excellent damping properties, good toughness, and well-developed pore structures, which have a broad application prospect in sound absorption field. This work aims to summarize the recent progress of fabrication and structure for PU porous materials in sound absorption application. The sound absorption mechanisms of porous materials are introduced. Different kinds of structure for typical PU porous materials in sound absorption application are covered and highlighted, which include PU foam, modified PU porous materials, aerogel, templated PU, and special PU porous materials. Finally, the development direction and existing problems of PU material in sound absorption application are briefly prospected. It can be expected that porous PU with high sound absorption coefficient can be obtained by using some facile methods. The design and accurate regulation of porous structures or construction of multilayer sound absorption structure is favorably recommended to fulfill the high demand of industrial and commercial applications in the future work.

Ent Manifestations in Sculptors of South Chennai, India: A Cross Sectional Observational Study

Despite the National Occupational Safety and Legislation Act 2020's implementation, reports of workplace accidents are rising in India. Various ear, nose, and throat conditions have been linked to a wide range of physico-chemical variables. Due to a lack of training, inadequate knowledge, a lack of awareness of occupational health and safety risks, or a lack of accessibility to or use of personal protective equipment (PPE), sculptors are frequently exposed to a variety of physical, compound and unplanned risks, chemical, and accidental hazards. The study aimed to assess the various ear, nose and throat manifestations like noise induced hearing loss, occupational rhinitis and non-infectious pharyngitis among the sculptors working in the southern part of Chennai. This observational study was performed in a total of 110 sculptors. Demographic data like age, education, duration of occupation, use of PPE like face mask, ear plug during work hours, whether sculpting is a family occupation or first generation sculptor. A detailed history and thorough ENT examination was performed with pure tone audiometry (PTA), diagnostic nasal endoscopy (DNE) and videolaryngoscopy (VLS). If any problem is detected they will be treated accordingly. Most of them (70%) were in the age group of 21-40 years but 71% of them are sculptors for more than 15 years which infers introduction to the occupation at an early age. The reason for this could be more than 80% of them possessed the heritage of sculpting as their family occupation. Duration of occupation was significantly associated with chronic rhinitis (P value was 0.002) and NIHL (P value was 0.002) whereas education and use of PPE like face mask or ear plugs were not associated with ENT manifestations. This study focuses on the sculptors' working habits, their ignorance of safety precautions, and an assessment of the numerous ENT ailments. These manifestations showed a strong correlation to exposure time. To prevent the issues from becoming more severe, regular medical monitoring is required for early detection and intervention.

Investigation of critical factors influencing the underestimation of hearing loss predicted by the ISO 1999 predicting model

OBJECTIVE

To analyze factors influencing the underestimation of noise-induced permanent threshold shift (NIPTS) among manufacturing workers, providing baseline data for revising noise exposure standard.

DESIGN

A cross-sectional study was designed with 2702 noise-exposed workers from 35 enterprises from 10 industries. Personal noise exposure level(LAeq,8h) and noise kurtosis level were determined by a noise dosimeter. Questionnaires and hearing loss tests were performed for each subject. The predicted NIPTS was calculated using the ISO 1999:2013 model for each participant, and the actual measured NIPTS was corrected for age and sex. The factors influencing the underestimation of NIPTS were investigated.

RESULTS

The predicted NIPTS at each test frequency (0.5, 1, 2, 3, 4, or 6kHz) and mean NIPTS at 2, 3, 4, and 6kHz (NIPTS2346) using the ISO 1999:2013 model were significantly lower than their corresponding measured NIPTS, respectively (P < 0.001). The ISO model significantly underestimated the NIPTS2346 by 12.36 dB HL. The multiple linear regression analysis showed that noise exposure level, exposure duration, age, and kurtosis could affect the degree of underestimation of NIPTS2346. The generalized additive model (GAM) with (penalized) spline components showed nonlinear relationships between critical factors (age, exposure duration, noise level, and kurtosis) and the underestimated NIPTS2346.The underestimated NIPTS2346 decreased with an increase in exposure duration (especially over ten years). There was no apparent trend in the underestimated NIPTS2346 with age. The underestimated NIPTS2346 decreased with the increased noise level [especially > 90 dB(A)]. The underestimated NIPTS2346 increased with an increase in noise kurtosis after adjusting for the noise exposure level and exposure duration and ultimately exhibiting a linear regression relationship.

CONCLUSIONS

The ISO 1999 predicting model significantly underestimated the noise-induced hearing loss among manufacturing workers. The degree of underestimation became more significant at the noise exposure condition of fewer than ten years, less than 90 dB(A), and higher kurtosis levels. It is necessary to apply kurtosis to adjust the underestimation of hearing loss and consider the applying condition of noise energy metrics when using the ISO predicting model.

The role of kurtosis and kurtosis-adjusted energy metric in occupational noise-induced hearing loss among metal manufacturing workers

Background

Noise energy has been well-established to increase the risk of occupational noise-induced hearing loss (NIHL). However, the role of noise temporal structure (expressed by kurtosis) or its combination with energy metrics (e.g., kurtosis-adjusted cumulative noise exposure, adj-CNE) in occupational NIHL was still unclear.

Methods

A cross-sectional survey of 867 Chinese workers, including 678 metal manufacturing workers and 189 workers exposed to Gaussian noise, was conducted. Noise energy metrics, including L_Aeq,8h and CNE, kurtosis (β), and adj-CNE were used to quantify noise exposure levels. Noise-induced permanent threshold shift at frequencies 3, 4, and 6 kHz (NIPTS₃₄₆) and the prevalence of high-frequency NIHL (HFNIHL%) were calculated for each participant. The dose-response relationship between kurtosis or adj-CNE and occupational NIHL was observed.

Results

Among 867 workers, different types of work had specific and independent noise energy and kurtosis values (p > 0.05). HFNIHL% increased with an increase in exposure duration (ED), L_Aeq,8h, CNE, or kurtosis (p < 0.01), and there were strong linear relationships between HFNIHL% and ED (coefficient of determination [R²] = 0.963), CNE (R² = 0.976), or kurtosis (R² = 0.938, when CNE < 100 dB(A)∙year). The "V" shape notching extent in NIPTS became deeper with increasing kurtosis when CNE < 100 dB(A)∙year and reached the notching bottom at the frequency of 4 or 6 kHz. The workers exposed to complex noise (β ≥ 10) had a higher risk of NIHL than those exposed to Gaussian noise (β < 10) at the frequencies of 3, 4, 6, and 8 kHz (OR > 2, p < 0.01). Moreover, HFNIHL% increased with adj-CNE (p < 0.001). There were strong linear relationships between NIHL and adj-CNE or CNE when β ≥ 10 (R²_adj-CNE > R²_CNE). After CNE was adjusted by kurtosis, average differences in NIPTS₃₄₆ or HFNIHL% between the complex and Gaussian noise group were significantly reduced (p < 0.05).

Conclusion

Kurtosis was a key factor influencing occupational NIHL among metal manufacturing workers, and its combination with energy metrics could assess the risk of NIHL more effectively than CNE alone.

Music as a unique source of noise-induced hearing loss

Music is among the most important artistic, cultural, and entertainment modalities in any society. With the proliferation of music genres and the technological advances that allow people to consume music in any location and at any time, music over-exposure has become a significant public health issue. Music-induced hearing loss has a great deal in common with noise-induced hearing loss. However, there are important differences that make music a unique insult to the auditory system and a unique threat to public health. Its unique properties also make it a potentially valuable asset in sound conditioning paradigms. This review discusses hearing loss from noise and music, comparing and contrasting the two. Recent research on music-induced hearing loss is reviewed, followed by discussion of the differences in music-induced hearing loss between performers and consumers. The review concludes with a discussion of the potential of music as a sound conditioning stimulus to protect against acquired hearing loss.

Diagnosing Noise-Induced Hearing Loss Sustained During Military Service Using Deep Neural Networks

The diagnosis of noise-induced hearing loss (NIHL) is based on three requirements: a history of exposure to noise with the potential to cause hearing loss; the absence of known causes of hearing loss other than noise exposure; and the presence of certain features in the audiogram. All current methods for diagnosing NIHL have involved examination of the typical features of the audiograms of noise-exposed individuals and the formulation of quantitative rules for the identification of those features. This article describes an alternative approach based on the use of multilayer perceptrons (MLPs). The approach was applied to databases containing the ages and audiograms of individuals claiming compensation for NIHL sustained during military service (M-NIHL), who were assumed mostly to have M-NIHL, and control databases with no known exposure to intense sounds. The MLPs were trained so as to classify individuals as belonging to the exposed or control group based on their audiograms and ages, thereby automatically identifying the features of the audiogram that provide optimal classification. Two databases (noise exposed and nonexposed) were used for training and validation of the MLPs and two independent databases were used for evaluation and further analyses. The best-performing MLP was one trained to identify whether or not an individual had M-NIHL based on age and the audiogram for both ears. This achieved a sensitivity of 0.986 and a specificity of 0.902, giving an overall accuracy markedly higher than for previous methods.

Epidemiological characteristics of hearing loss associated with noise temporal structure among manufacturing workers

Objective: This study aimed to investigate the epidemiological characteristics of occupational noise-induced hearing loss (NIHL) among manufacturing workers, and to provide evidence for diagnosing and preventing occupational hearing loss caused by complex noise, which is different from Gaussian noise in temporal structure.

Methods: One thousand and fifty manufacturing workers exposed to occupational noise were recruited in a cross-sectional survey. Exposure characteristics and epidemiological distribution of hearing loss and noise exposure metrics (noise energy and kurtosis) were investigated, and the relationship between noise exposure and hearing loss was analyzed. The effects of kurtosis on hearing threshold shift across different frequencies and on NIHL development with exposure duration and noise intensity were also investigated.

Results: Each type of work had specific noise exposure metrics. Noise intensity and kurtosis were independent parameters (r = −0.004, p = 0.885). The prevalence of NIHL and the hearing threshold level had a specific distribution in different types of work. Kurtosis deepened the hearing notch at high frequencies and accelerated the formation of early hearing loss. The effect of exposure duration and noise intensity on the prevalence of high-frequency NIHL (i.e., at 3, 4, 6, and 8 kHz) for manufacturing workers increased with kurtosis in workers with noise exposure duration of less than 10 years and with L_Aeq.8h between 80 and 90 dB(A). Male (OR = 1.557, 95%CI = 1.141–2.124), age (OR = 1.033, 95%CI = 1.014–1.052), exposure duration (OR = 1.072, 95%CI = 1.038–1.107), kurtosis (OR = 1.002, 95%CI = 1.001–1.003), and noise intensity (L_Aeq.8h; OR = 1.064, 95%CI = 1.044–1.084) were risk factors for high-frequency NIHL. The speech-frequency NIHL (i.e., at 0.5, 1, and 2 kHz) risk of workers exposed to manufacturing noise was related to age (OR = 1.071, 95%CI = 1.043–1.100). There were no statistically significant associations between speech-frequency NIHL and sex, noise exposure duration, kurtosis, and noise intensity (L_Aeq.8h).

Conclusion: The high-frequency NIHL prevalence among manufacturing workers is associated with sex, age, exposure duration, noise intensity, and temporal structure of noise, while the speech-frequency NIHL prevalence is associated with age. Kurtosis strengthens the association of noise exposure duration and noise intensity with high-frequency hearing loss. The influence of noise temporal structure should be considered in the diagnosis and early prevention of occupational hearing loss caused by complex noise.