Author: Hart, Breanna N; Jeng, Fuh-Cherng
Title: A Demonstration of Machine Learning in Detecting Frequency Following Responses in American Neonates. Cord-id: x1fi1sn6 Document date: 2020_9_22
ID: x1fi1sn6
Snippet: In this study, we sought to evaluate the efficiencies of multiple machine learning algorithms in detecting neonates' Frequency Following Responses (FFRs). We recorded continuous brainwaves from 43 American neonates in response to a pre-recorded monosyllable/i/with a rising frequency contour. Recordings were classified into response and no response categories. Six response features were extracted from each recording and served as predictors in FFR identification. Twenty-three supervised machine l
Document: In this study, we sought to evaluate the efficiencies of multiple machine learning algorithms in detecting neonates' Frequency Following Responses (FFRs). We recorded continuous brainwaves from 43 American neonates in response to a pre-recorded monosyllable/i/with a rising frequency contour. Recordings were classified into response and no response categories. Six response features were extracted from each recording and served as predictors in FFR identification. Twenty-three supervised machine learning algorithms with mean efficiency values of 86.0%, 94.4%, 97.2%, and 97.5% when 1, 10, 100, and 1000 random iterations were implemented, respectively. These high efficiency values obtained from the neonatal FFRs demonstrate that machine learning algorithms can help assess pitch processing in neonates and can be applied to auditory screening and intervention services for neonates at risk for disorders associated with decreased pitch processing.
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