Optimal channel selection for analysis of EEG-sleep patterns of neonates

Authors:
Alexandra Piryatinska;Wojbor A. Woyczynski;Mark S. Scher;Kenneth A. Loparo
Affiliations:
Department of Mathematics, San Francisco State University, San Francisco, CA 94132, United States;Department of Statistics, Center for Stochastic and Chaotic Processes in Science and Technology, Case Western Reserve University, Cleveland, OH 44106, United States;Department of Pediatric Neurology, Case Western Reserve University, Cleveland, OH 44106, United States;Department of Electrical and Computer Science, Case Western Reserve University, Cleveland, OH 44106, United States
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

Citing 2
Cited 1

Automated detection of neonate EEG sleep stages

Computer Methods and Programs in Biomedicine
Multivariate analysis of full-term neonatal polysomnographic data

IEEE Transactions on Information Technology in Biomedicine

Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal

Computers in Biology and Medicine

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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.