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• W3094320922 endingPage "108101" @default.
• W3094320922 startingPage "108101" @default.
• W3094320922 abstract "Successful mapping of meaningful labels to sound input requires accurate representation of that sound’s acoustic variances in time and spectrum. For some individuals, such as children or those with hearing loss, having an objective measure of the integrity of this representation could be useful. Classification is a promising machine learning approach which can be used to objectively predict a stimulus label from the brain response. This approach has been previously used with auditory evoked potentials (AEP) such as the frequency following response (FFR), but a number of key issues remain unresolved before classification can be translated into clinical practice. Specifically, past efforts at FFR classification have used data from a given subject for both training and testing the classifier. It is also unclear which components of the FFR elicit optimal classification accuracy. To address these issues, we recorded FFRs from 13 adults with normal hearing in response to speech and music stimuli. We compared labeling accuracy of two cross-validation classification approaches using FFR data: (1) a more traditional method combining subject data in both the training and testing set, and (2) a “leave-one-out” approach, in which subject data is classified based on a model built exclusively from the data of other individuals. We also examined classification accuracy on decomposed and time-segmented FFRs. Our results indicate that the accuracy of leave-one-subject-out cross validation approaches that obtained in the more conventional cross-validation classifications while allowing a subject’s results to be analysed with respect to normative data pooled from a separate population. In addition, we demonstrate that classification accuracy is highest when the entire FFR is used to train the classifier. Taken together, these efforts contribute key steps toward translation of classification-based machine learning approaches into clinical practice." @default.
• W3094320922 created "2020-10-29" @default.
• W3094320922 creator A5002580793 @default.
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• W3094320922 creator A5059513758 @default.
• W3094320922 creator A5074295257 @default.
• W3094320922 date "2020-12-01" @default.
• W3094320922 modified "2023-09-30" @default.
• W3094320922 title "Factors influencing classification of frequency following responses to speech and music stimuli" @default.
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• W3094320922 doi "https://doi.org/10.1016/j.heares.2020.108101" @default.
• W3094320922 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33142106" @default.
• W3094320922 hasPublicationYear "2020" @default.
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