A machine learning approach for distinguishing hearing perception level using auditory evoked potentials

Paulraj, Michael Gabriel; Subramaniam, Kamalraj; Yaccob, Sazali Bin; Adom, Abdul Hamid; Hema, C. R.

doi:10.1109/iecbes.2014.7047661

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…From the 99 unique publications, we identified 14 as highly relevant. The main reasons for dismissing papers were that they did not apply ML classification methods, 28 investigated other diseases from NBS programs such as hearing disabilities 29 or did not use data obtained from MS/MS. 30 Publications from different screening centers in Europe, [12][13][14][15][16]19 Asia, 7,17,18,20,21,23,24,26 and North America 22,25,27 are reviewed in this work.…”

Section: Resultsmentioning

confidence: 99%

Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review

et al. 2022

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The development and continuous optimization of newborn screening (NBS) programs remains an important and challenging task due to the low prevalence of screened diseases and high sensitivity requirements for screening methods. Recently, different machine learning (ML) methods have been applied to support NBS. However, most studies only focus on single diseases or specific ML techniques making it difficult to draw conclusions on which methods are best to implement. Therefore, we performed a systematic literature review of peer‐reviewed publications on ML‐based NBS methods. Overall, 125 related papers, published in the past two decades, were collected for the study, and 17 met the inclusion criteria. We analyzed the opportunities and challenges of ML methods for NBS including data preprocessing, classification models and pattern recognition methods based on their underlying approaches, data requirements, interpretability on a modular level, and performance. In general, ML methods have the potential to reduce the false positive rate and identify so far unknown metabolic patterns within NBS data. Our analysis revealed, that, among the presented, logistic regression analysis and support vector machines seem to be valuable candidates for NBS. However, due to the variety of diseases and methods, a general recommendation for a single method in NBS is not possible. Instead, these methods should be further investigated and compared to other approaches in comprehensive studies as they show promising results in NBS applications.

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Section: Resultsmentioning

confidence: 99%

Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review

et al. 2022

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“…The regression forest model provided a 94% accuracy rate. Paulraj et al [43] used Higuchi's fractal method to extract features from auditory evoked potential signals. Charih et al [13] employed a Gaussian mixture modelbased approach to detect atypical audiograms.…”

Section: Related Workmentioning

confidence: 99%

Automated hearing impairment diagnosis using machine‐learning: An open‐source software development undergraduate research project

Taylor,

Sheikh

2024

Comp Applic In Engineering

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Approximately 700 million people will have disabling hearing loss by 2050. Underdeveloped and developing countries, which encompass a considerable proportion of people with disabling hearing impairment, have a sparse number of audiologists and otolaryngologists. The lack of specialists leaves most hearing impairments undiagnosed for a long time, resulting in negative societal and economic impacts. In this article, we propose an automated hearing impairment diagnosis software—based on machine learning—to support audiologists and otolaryngologists in accurately and efficiently diagnosing and classifying hearing loss. We present the design, implementation, and performance analysis of an open‐source automated hearing impairment diagnosis software, which consists of two modules: a hearing test data‐generation module and a machine‐learning model. The data‐generation module produces a diverse and exhaustive data set for training and evaluating the machine‐learning model. By employing multiclass and ultilabel classification techniques to learn from the hearing test data, the model can swiftly predict the type, degree, and configuration of hearing loss with high reliability. Our proposed machine‐learning model demonstrates promising results with a prediction time of 634 ms, a log‐loss reduction rate of 0.9848 and accuracy, precision, recall, and f1‐score of 1.0000—showing the model's applicability to assist audiologists and otolaryngologists in rapidly and accurately classifying the type, degree, and configuration of hearing loss. In addition to the technical contributions, this article also highlights the importance of involving undergraduate students in open‐source software development projects which have a direct impact on improving the quality of human life.

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“…In the study by Paulraj M P and colleagues, the responses of newborn babies' brains to different decibels and frequencies of sounds were investigated using EEG. Studies using MLP and EN, classification algorithms of artificial neural networks, resulted in a detection accuracy rate of 79.99% for the left ear and 82.78% for the right ear [16].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals

et al. 2023

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This study uses machine learning to perform the hearing test (audiometry) processes autonomously with EEG signals. Sounds with different amplitudes and wavelengths given to the person tested in standard hearing tests are assigned randomly with the interface designed with MATLAB GUI. The person stated that he heard the random size sounds he listened to with headphones but did not take action if he did not hear them. Simultaneously, EEG (electro-encephalography) signals were followed, and the waves created in the brain by the sounds that the person attended and did not hear were recorded. EEG data generated at the end of the test were pre-processed, and then feature extraction was performed. The heard and unheard information received from the MATLAB interface was combined with the EEG signals, and it was determined which sounds the person heard and which they did not hear. During the waiting period between the sounds given via the interface, no sound was given to the person. Therefore, these times are marked as not heard in EEG signals. In this study, brain signals were measured with Brain Products Vamp 16 EEG device, and then EEG raw data were created using the Brain Vision Recorder program and MATLAB. After the data set was created from the signal data produced by the heard and unheard sounds in the brain, machine learning processes were carried out with the PYTHON programming language. The raw data created with MATLAB was taken with the Python programming language, and after the pre-processing steps were completed, machine learning methods were applied to the classification algorithms. Each raw EEG data has been detected by the Count Vectorizer method. The importance of each EEG signal in all EEG data has been calculated using the TF-IDF (Term Frequency-Inverse Document Frequency) method. The obtained dataset has been classified according to whether people can hear the sound. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms have been applied in the analysis. The algorithms selected in our study were preferred because they showed superior performance in ML and succeeded in analyzing EEG signals. Selected classification algorithms also have features of being used online. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms were used. In the analysis of EEG signals, Light Gradient Strengthening Machine (LGBM) was obtained as the best method. It was determined that the most successful algorithm in prediction was the prediction of the LGBM classification algorithm, with a success rate of 84%. This study has revealed that hearing tests can also be performed using brain waves detected by an EEG device. Although a completely independent hearing test can be created, an audiologist or doctor may be needed to evaluate the results.

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A machine learning approach for distinguishing hearing perception level using auditory evoked potentials

Cited by 11 publications

References 14 publications

Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review

Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review

Automated hearing impairment diagnosis using machine‐learning: An open‐source software development undergraduate research project

A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals

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