Random Forest-based Algorithm for Sleep Spindle Detection in Infant EEG
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|Title:||Random Forest-based Algorithm for Sleep Spindle Detection in Infant EEG||Authors:||Wei, Lan; Ventura, Soraia; Lowery, Madeleine M.; Mooney, Catherine; et al.||Permanent link:||http://hdl.handle.net/10197/12166||Date:||24-Jul-2020||Online since:||2021-05-18T12:07:26Z||Abstract:||Sleep spindles are associated with normal brain development, memory consolidation and infant sleep-dependent brain plasticity and can be used by clinicians in the assessment of brain development in infants. Sleep spindles can be detected in EEG, however, identifying sleep spindles in EEG recordings manually is very time-consuming and typically requires highly trained experts. Research on the automatic detection of sleep spindles in infant EEGs has been limited to-date. In this study, we present a novel supervised machine learning-based algorithm to detect sleep spindles in infant EEG recordings. EEGs collected from 141 ex-term born infants and 6 ex-preterm born infants, recorded at 4 months of age (adjusted), were used to train and test the algorithm. Sleep spindles were annotated by experienced clinical physiologists as the gold standard. The dataset was split into training (81 ex-term), validation (30 ex-term), and testing (30 ex-term + 6 ex-preterm) set. 15 features were selected for input into a random forest algorithm. Sleep spindles were detected in the ex-term infant EEG test set with 92.1% sensitivity and 95.2% specificity. For ex-preterm born infants, the sensitivity and specificity were 80.3% and 91.8% respectively. The proposed algorithm has the potential to assist researchers and clinicians in the automated analysis of sleep spindles in infant EEG.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||IEEE||Copyright (published version):||2020 IEEE||Keywords:||Humans; Electroencephalography; Sensitivity and specificity; Sleep; Algorithms; Infants; Newborns; Memory consolidation||DOI:||10.1109/EMBC44109.2020.9176339||Language:||en||Status of Item:||Peer reviewed||Is part of:||Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS||Conference Details:||The Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020), Virtual Conference, 20-24 July 2020||ISBN:||9781728119908||ISSN:||1557-170X||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
Electrical and Electronic Engineering Research Collection
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