Auditory characteristics of noise-exposed memberscrossing age-related groups

Chenqing, Liu; Ding, Di; Zhu, Yushan; Wang, Hongyang; Cheng, Xiaoting; Zhang, Zhenhua; Juyang, Cao; Zhai, Shumei; Yu, Ning

doi:10.1016/j.joto.2018.05.003

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…Both aging and noise contribute to hearing loss, and therefore, it is unclear whether high frequency hearing loss should be regarded as a result of noise. 9 - 11 In our study, the hearing threshold tended to decrease from low to high frequencies in patients with and without tinnitus.…”

Section: Discussionsupporting

confidence: 45%

“…Most previous studies of occupational NIHL included relatively few patients. Although the hearing threshold notch is formed at 4 kHz in early-stage patients, gradually affecting the 3-6 kHz range, 9 presbycusis in elderly patients results in severe hearing loss at high frequency and slower notch formation. 10 As NIHL progresses, hearing is also lost at low frequencies.…”

Section: Discussionmentioning

confidence: 99%

“…Tinnitus in patients with acoustic trauma appears in the transition region between the damaged and intact parts of the organ of Corti. 9 Pathologically, these findings are related to changes in the outer hair cells at the site of injury in patients with occupational noise exposure who may repeatedly experience acoustic trauma. Thus, tinnitus of about 4 kHz may be a result of the most serious type of damage to the inner ear.…”

Section: Discussionmentioning

confidence: 99%

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Audiologic Characteristics of Hearing and Tinnitus in Occupational Noise-Induced Hearing Loss

Kang

Zhe

et al. 2021

Int Adv Otol

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Background : To analyze the characteristics of patients with noise-induced hearing loss (NIHL) by comparing audiologic test findings between groups with and without tinnitus. Methods: This study involved patients with noise-induced hearing loss (NIHL) who presented to the otolaryngology clinic between January 2016 and April 2019. Tests including 3 pure-tone audiometry (PTA) tests at intervals greater than 1 week, and auditory brainstem response (ABR) were evaluated and patients were screened for tinnitus. The tinnitus patients had otoacoustic emission. Comparison was done between the tinnitus group and the group without tinnitus. Results : Of the 730 subjects with NIHL, 389 had tinnitus. PTA showed significantly higher thresholds at 2 kHz to 8 kHz in the tinnitus group. Although ABR tests tended to show more prolonged I, III, and V latency in the tinnitus group, the differences were not statistically significant. Distortion product otoacoustic emissions (DPOAE) showed more abnormalities at 3 kHz, and 4 kHz than at 1 kHz and 2 kHz. Transient otoacoustic emission (TEOAE) showed abnormal findings in both ears. Conclusion: In NIHL, hearing loss was more severe in patients with, than without tinnitus. DPOAE showed more abnormalities at 3 kHz, 4 kHz, and 6 kHz than at 1 kHz and 2 kHz, and TEOAE was abnormal at all frequencies.

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Section: Discussionsupporting

confidence: 45%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Audiologic Characteristics of Hearing and Tinnitus in Occupational Noise-Induced Hearing Loss

Kang

Zhe

et al. 2021

Int Adv Otol

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Noise Prediction Using Machine Learning with Measurements Analysis

Wen

Huang

2020

Applied Sciences

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The noise prediction using machine learning is a special study that has recently received increased attention. This is particularly true in workplaces with noise pollution, which increases noise exposure for general laborers. This study attempts to analyze the noise equivalent level (Leq) at the National Synchrotron Radiation Research Center (NSRRC) facility and establish a machine learning model for noise prediction. This study utilized the gradient boosting model (GBM) as the learning model in which past noise measurement records and many other features are integrated as the proposed model makes a prediction. This study analyzed the time duration and frequency of the collected Leq and also investigated the impact of training data selection. The results presented in this paper indicate that the proposed prediction model works well in almost noise sensors and frequencies. Moreover, the model performed especially well in sensor 8 (125 Hz), which was determined to be a serious noise zone in the past noise measurements. The results also show that the root-mean-square-error (RMSE) of the predicted harmful noise was less than 1 dBA and the coefficient of determination (R2) value was greater than 0.7. That is, the working field showed a favorable noise prediction performance using the proposed method. This positive result shows the ability of the proposed approach in noise prediction, thus providing a notification to the laborer to prevent long-term exposure. In addition, the proposed model accurately predicts noise future pollution, which is essential for laborers in high-noise environments. This would keep employees healthy in avoiding noise harmful positions to prevent people from working in that environment.

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Auditory characteristics of noise-exposed memberscrossing age-related groups

Cited by 2 publications

References 12 publications

Audiologic Characteristics of Hearing and Tinnitus in Occupational Noise-Induced Hearing Loss

Audiologic Characteristics of Hearing and Tinnitus in Occupational Noise-Induced Hearing Loss

Noise Prediction Using Machine Learning with Measurements Analysis

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