title: Optimal channel selection for analysis of EEG-sleep patterns of neonates.
abstract: This paper extends our previous work on automated detection and classification of neonate EEG sleep stages. In [19] we adapted and integrated a range of computational, mathematical and statistical tools for the analysis of neonatal electroencephalogram (EEG) sleep recordings with the aim of facilitating the assessment of neonatal brain maturation and dismaturity by studying the structure and temporal patterns of their sleep. That work relied on algorithms using a single channel of EEG. The present paper builds on our previous work by incorporating a larger selection of EEG channels that capture both the spatial distribution and temporal patterns of EEG during sleep. Using a multivariate analysis approach, we obtain the "optimal" selection of the EEG channels and characteristics that are most suitable for EEG sleep state separation.
authors: Department of Mathematics, San Francisco State University, CA 94132, United States. alpiryat@sfsu.edu
authors: Alexandra
authors: A
authors: Piryatinska
authors: Wojbor A
authors: WA
authors: Woyczynski
authors: Mark S
authors: MS
authors: Scher
authors: Kenneth A
authors: KA
authors: Loparo
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D007231:Infant, Newborn / Q000502:physiology*; D007234:Infant, Premature / Q000502:physiology; D008959:Models, Neurological; D015999:Multivariate Analysis; D012894:Sleep Stages / Q000502:physiology*