Classification of audiograms in the prevention of noise-induced hearing loss: A clinical perspective

Musiba, Zumbi

doi:10.4102/sajcd.v67i2.691

Cited by 5 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, employers are required to implement administrative controls to minimize workers’ exposure to excessive noise ( Musiba, 2015 ). These controls may include limiting exposure time, scheduling rest breaks in quieter areas, and implementing job rotation to reduce individual exposure levels.…”

Section: Introductionmentioning

confidence: 99%

Development of an artificial intelligence based occupational noise induced hearing loss early warning system for mine workers

Madahana,

Ekoru,

Sebothoma

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

IntroductionOccupational Noise Induced Hearing Loss (ONIHL) is one of the most prevalent conditions among mine workers globally. This reality is due to mine workers being exposed to noise produced by heavy machinery, rock drilling, blasting, and so on. This condition can be compounded by the fact that mine workers often work in confined workspaces for extended periods of time, where little to no attenuation of noise occurs. The objective of this research work is to present a preliminary study of the development of a hearing loss, early monitoring system for mine workers.MethodologyThe system consists of a smart watch and smart hearing muff equipped with sound sensors which collect noise intensity levels and the frequency of exposure. The collected information is transferred to a database where machine learning algorithms namely the logistic regression, support vector machines, decision tree and Random Forest Classifier are used to classify and cluster it into levels of priority. Feedback is then sent from the database to a mine worker smart watch based on priority level. In cases where the priority level is extreme, indicating high levels of noise, the smart watch vibrates to alert the miner. The developed system was tested in a mock mine environment consisting of a 67 metres tunnel located in the basement of a building whose roof top represents the “surface” of a mine. The mock-mine shape, size of the tunnel, steel-support infrastructure, and ventilation system are analogous to deep hard-rock mine. The wireless channel propagation of the mock-mine is statistically characterized in 2.4–2.5 GHz frequency band. Actual underground mine material was used to build the mock mine to ensure it mimics a real mine as close as possible. The system was tested by 50 participants both male and female ranging from ages of 18 to 60 years.Results and discussionPreliminary results of the system show decision tree had the highest accuracy compared to the other algorithms used. It has an average testing accuracy of 91.25% and average training accuracy of 99.79%. The system also showed a good response level in terms of detection of noise input levels of exposure, transmission of the information to the data base and communication of recommendations to the miner. The developed system is still undergoing further refinements and testing prior to being tested in an actual mine.

show abstract

Section: Introductionmentioning

confidence: 99%

Development of an artificial intelligence based occupational noise induced hearing loss early warning system for mine workers

Madahana,

Ekoru,

Sebothoma

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Musiba 9 has classified audiograms based on UKHSE (United Kingdom Health and Safety Executive) categorization scheme. The sum of pure tone audiometry test hearing levels at frequencies 1 kHz, 2 kHz, 3 kHz, 4 kHz and 6 kHz, were obtained.…”

Section: Introductionmentioning

confidence: 99%

“…The audiograms were classified into eight levels and the change in hearing ability over time was recorded based on these classes. Musiba 9 and Cruickshanks 10 did not implement any intelligent solutions as they counted on the experience of the specialists in the field.…”

Section: Introductionmentioning

confidence: 99%

Data-driven audiogram classifier using data normalization and multi-stage feature selection

Elkhouly

Andrew

Rahim

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Audiograms are used to show the hearing capability of a person at different frequencies. The filter bank in a hearing aid is designed to match the shape of patients’ audiograms. Configuring the hearing aid is done by modifying the designed filters’ gains to match the patient’s audiogram. There are few problems faced in achieving this objective successfully. There is a shortage in the number of audiologists; the filter bank hearing aid designs are complex; and, the hearing aid fitting process is tiring. In this work, a machine learning solution is introduced to classify the audiograms according to the shapes based on unsupervised spectral clustering. The features used to build the ML model are peculiar and describe the audiograms better. Different normalization methods are applied and studied statistically to improve the training data set. The proposed Machine Learning (ML) algorithm outperformed the current existing models, where, the accuracy, precision, recall, specificity, and F-score values are higher. The reason for the better performance is the use of multi-stage feature selection to describe the audiograms precisely. This work introduces a novel ML technique to classify audiograms according to the shape, which, can be integrated to the future and existing studies to change the existing practices in classifying audiograms.

show abstract

“…Inform. Visualization, 7(4) -December 2023 2523-2531 [7]. Figure 1 shows six categories of audiogram results [8]: Normal Hearing Ability, Mild Hearing Loss, Moderate Hearing Loss, Moderately Severe Hearing Loss, Severe Hearing Loss, and Profound Hearing Loss.…”

Section: Introductionmentioning

confidence: 99%

The Implementation of the K-Medoid Clustering for Grouping Hearing Loss Function on Excessive Smartphone Use

Wahyudi,

Meidelfi,

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

During the current pandemic, smartphones have become a means of learning for all students in Indonesia, including high school students. Students use smartphones to send assignments, learn via video calls, and conduct online exams. The prolonged use of smartphones, from the beginning of learning hours in the morning to study hours in the evening, has a terrible impact on the ear health of high school students in Padang. Excessive smartphone use caused a decrease in the student's hearing function. Therefore, this study aims to group the audiometry results of high school students in Padang who have a hearing loss function. The audiogram result is only performed as the result of a frequency test of the subject's hearing in both the left and right ear. Conventionally, an otolaryngologist concluded the final decision of hearing loss ability. This research proposed an automatic classification of audiometry results using machine learning methods. The K-Medoids clustering was selected to classify the audiometry data in this research. Of 210 audiometry data, 91 data is confirmed by an otolaryngologist as valid data. By using the K-Medoids clustering, 93 data is classified into Normal hearing, Mild Hearing loss, and Moderate Hearing loss. The proposed model successfully grouped the audiometry data into three categories. The confusion matrix is applied to measure the model performance, which has 28,3% accuracy, 64,3% precision, and 21,4% recall.

show abstract

Classification of audiograms in the prevention of noise-induced hearing loss: A clinical perspective

Cited by 5 publications

References 14 publications

Development of an artificial intelligence based occupational noise induced hearing loss early warning system for mine workers

Development of an artificial intelligence based occupational noise induced hearing loss early warning system for mine workers

Data-driven audiogram classifier using data normalization and multi-stage feature selection

The Implementation of the K-Medoid Clustering for Grouping Hearing Loss Function on Excessive Smartphone Use

Contact Info

Product

Resources

About