Comparison of machine learning models to classify Auditory Brainstem Responses recorded from children with Auditory Processing Disorder

Wimalarathna, Hasitha; Veeranna, Sangamanatha Ankmnal; Allan, Chris; Agrawal, Sumit; Allen, Philip A.; Samarabandu, Jagath; Ladak, Hanif M.

doi:10.1016/j.cmpb.2021.105942

Cited by 16 publications

(8 citation statements)

References 29 publications

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“…Their results suggest that EGB may be well suited for the future development of an automatic evaluation tool for clinical ABR waveform analysis. Taken together, the findings of Dobrowolski et al [32] and Wimalarath et al [18] suggest that automating speech-evoked ABR analysis using machine learning may provide a complementary means to help clinicians better diagnose their patients' hearing. On the other hand, a significant limitation for these studies is the size of the dataset available.…”

Section: E Applications Of ML Models To Classify Abrmentioning

confidence: 93%

“…Only [34], [18] used a CNN classifier. McKeraney and MacKinnon et al [34] examined 232 paired ABR waveforms of tone pips from eight normal-hearing subjects.…”

Section: B Cnn Based ML Models To Classify Abrmentioning

confidence: 99%

“…While clinicians frequently draw similar conclusions; however, differences can occur especially with less experienced audiologists [17]. In addition, the literature does not clearly describe normative data for some hearing impairments, such as auditory processing disorder (APD) [18]. Therefore, there is a need to automate the ABR analysis to support clinicians in making an accurate diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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On the Use of Machine Learning for Classifying Auditory Brainstem Responses: A Scoping Review

Osman

2021

IEEE Access

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Recent advances in machine learning have led to a surge of interest in classification of the auditory brainstem response. By conducting a search in the PubMed, Google Scholar, SpringerLink, ScienceDirect, and Scopus databases, it was possible to identify twelve studies that explored the use of machine learning to classify the auditory brainstem response as a complementary and objective method to (a) help clinicians better diagnose hearing impairment by discerning between healthy and pathological auditory brainstem response waveforms, (b) present a neural marker for potential applications in hearing aid tuning, and (c) provide a biometric marker for discriminating between subjects. A comparison between the studies presented in this review is not possible as they used different test subjects, group sizes, and stimuli, and evaluated ABR differently. Instead, the result of these studies will be presented and their limitations as well as their potential applications will be discussed. Overall, the findings of these studies suggest that ABR classification using machine learning is a promising tool for assessing patients with hearing loss, optimizing technologies for tuning hearing aids, and discriminating between subjects.

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Section: E Applications Of ML Models To Classify Abrmentioning

confidence: 93%

“…Only [34], [18] used a CNN classifier. McKeraney and MacKinnon et al [34] examined 232 paired ABR waveforms of tone pips from eight normal-hearing subjects.…”

Section: B Cnn Based ML Models To Classify Abrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Use of Machine Learning for Classifying Auditory Brainstem Responses: A Scoping Review

Osman

2021

IEEE Access

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“…ABR thresholds are generally determined as the lowest sound intensity at which a waveform is manually observed ( Sato et al, 2010 ; Muniak et al, 2018 ; Buran et al, 2020 ). Because automatic wave-detection algorithms ( Wimalarathna et al, 2021 ) should improve objectivity and reproducibility, here we added an adaptation of the 3SD/4SD method for determining the ABR thresholds. This method uses the SD of the pre-signal window.…”

Section: Methodsmentioning

confidence: 99%

Bats experience age-related hearing loss (presbycusis)

et al. 2023

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Hearing loss is a hallmark of aging, typically initially affecting the higher frequencies. In echolocating bats, the ability to discern high frequencies is essential. However, nothing is known about age-related hearing loss in bats, and they are often assumed to be immune to it. We tested the hearing of 47 wild Egyptian fruit bats by recording their auditory brainstem response and cochlear microphonics, and we also assessed the cochlear histology in four of these bats. We used the bats’ DNA methylation profile to evaluate their age and found that bats exhibit age-related hearing loss, with more prominent deterioration at the higher frequencies. The rate of the deterioration was ∼1 dB per year, comparable to the hearing loss observed in humans. Assessing the noise in the fruit bat roost revealed that these bats are exposed to continuous immense noise—mostly of social vocalizations—supporting the assumption that bats might be partially resistant to loud noise. Thus, in contrast to previous assumptions, our results suggest that bats constitute a model animal for the study of age-related hearing loss.

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“…Audiologists, to compensate for this limitation, use objective tests such as auditory brain stem response (ABR) [4,5]. ABRs are short-latency potentials evoked from the brain stem in response to acoustic stimulus [6] and can be recorded in a duration of less than 10-15 milliseconds (ms) after stimulus onset [2]. ABR is typically measured in an anechoic audiometric room using three surface electrodes on the scalp [5].…”

Section: Introduction 1backgroundmentioning

confidence: 99%