Machine learning approaches used to analyze auditory evoked responses from the human auditory brainstem: A systematic review

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

doi:10.1016/j.cmpb.2022.107118

Cited by 5 publications

(1 citation statement)

References 44 publications

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“…Artificial intelligence (AI) refers to the use of computers to automate complex tasks generally performed by humans [8,9]. Machine learning (ML) is a type of AI that is a powerful method of data analysis that is based on the concepts of learning and discovering data patterns rather than being programmed [10].…”

Section: Motivationmentioning

confidence: 99%

Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response

Özyurt¹,

Majidpour

Rashid³

et al. 2023

Preprint

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Section: Motivationmentioning

confidence: 99%

Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response

Özyurt¹,

Majidpour

Rashid³

et al. 2023

Preprint

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Automatic Recognition of Auditory Brainstem Response Waveforms Using a Deep Learning‐Based Framework

Liang,

Xu,

Liu

et al. 2024

Otolaryngol.--head neck surg.

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ObjectiveRecognition of auditory brainstem response (ABR) waveforms may be challenging, particularly for older individuals or those with hearing loss. This study aimed to investigate deep learning frameworks to improve the automatic recognition of ABR waveforms in participants with varying ages and hearing levels.Study DesignThe research used a descriptive study design to collect and analyze pure tone audiometry and ABR data from 100 participants.SettingThe research was conducted at a tertiary academic medical center, specifically at the Clinical Audiology Center of Tsinghua Chang Gung Hospital (Beijing, China).MethodsData from 100 participants were collected and categorized into four groups based on age and hearing level. Features from both time‐domain and frequency‐domain ABR signals were extracted and combined with demographic factors, such as age, sex, pure‐tone thresholds, stimulus intensity, and original signal sequences to generate feature vectors. An enhanced Wide&Deep model was utilized, incorporating the Light‐multi‐layer perceptron (MLP) model to train the recognition of ABR waveforms. The recognition accuracy (ACC) of each model was calculated for the overall data set and each group.ResultsThe ACC rates of the Light‐MLP model were 97.8%, 97.2%, 93.8%, and 92.0% for Groups 1 to 4, respectively, with a weighted average ACC rate of 95.4%. For the Wide&Deep model, the ACC rates were 93.4%, 90.8%, 92.0%, and 88.3% for Groups 1 to 4, respectively, with a weighted average ACC rate of 91.0%.ConclusionBoth the Light‐MLP model and the Wide&Deep model demonstrated excellent ACC in automatic recognition of ABR waveforms across participants with diverse ages and hearing levels. While the Wide&Deep model's performance was slightly poorer than that of the Light‐MLP model, particularly due to the limited sample size, it is anticipated that with an expanded data set, the performance of Wide&Deep model may be further improved.

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