Automated wave labelling of the auditory brainstem response using machine learning.
Objective: To compare the performance of a selection of machine learning algorithms, trained to label peaks I, III, and V of the auditory brainstem response (ABR) waveform. An additional algorithm was trained to provide a confidence measure related to the ABR wave latency estimates. Design: Secondar...
| Publicado en: | International Journal of Audiology Vol. 64; no. 7; pp. 766 - 772 |
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| Autores principales: | , , |
| Formato: | research tables/charts tracings Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jul2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186418829&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186418829 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14992027 JW2 jtl: International Journal of Audiology issn: 14992027 maglogo: Y pubinfo: dt: Jul2025 vid: 64 iid: 7 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 186418829 180066954 186418829 186418829 10.1080/14992027.2024.2404537 186418829 ppf: 766 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated wave labelling of the auditory brainstem response using machine learning. aug: au: McKearney, Richard M. Simpson, David M. Bell, Steven L. affil: Institute of Sound and Vibration Research, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK sug: subj: Evoked Potentials, Auditory, Brainstem Machine Learning Algorithms Neural Networks (Computer) Signal Processing, Computer Assisted Human Secondary Analysis Convolutional Neural Networks Waveforms Random Sample Wilcoxon Signed Rank Test Descriptive Statistics Confidence Intervals Funding Source ab: Objective: To compare the performance of a selection of machine learning algorithms, trained to label peaks I, III, and V of the auditory brainstem response (ABR) waveform. An additional algorithm was trained to provide a confidence measure related to the ABR wave latency estimates. Design: Secondary data analysis of a previously published ABR dataset. Five types of machine learning algorithm were compared within a nested k-fold cross-validation procedure. Study sample: A set of 482 suprathreshold ABR waveforms were used. These were recorded from 81 participants with audiometric thresholds within normal limits. Results: A convolutional recurrent neural network (CRNN) outperformed the other algorithms evaluated. The algorithm labelled 95.9% of ABR waves within ±0.1 ms of the target. The mean absolute error was 0.025 ms, averaged across the outer validation folds of the nested cross-validation procedure. High confidence levels were generally associated with greater wave-labelling accuracy. Conclusions: Machine learning algorithms have the potential to assist clinicians with ABR interpretation. The present work identifies a promising machine learning approach, but any algorithm to be used in clinical practice would need to be trained on a large, accurately labelled, heterogeneous dataset and evaluated in clinical settings in follow-on work. pubtype: Academic Journal doctype: research tables/charts tracings Journal Article ougenre: Unknown language: English refInfo: holdings: @attributes: islocal: N |
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