Automated detection of neonate EEG sleep stages

Authors:
Alexandra Piryatinska;Gyorgy Terdik;Wojbor A. Woyczynski;Kenneth A. Loparo;Mark S. Scher;Anatoly Zlotnik
Affiliations:
Department of Mathematics, San Francisco State University, San Francisco, CA 94132, United States;Institute of Mathematics and Informatics, University of Debrecen, Debrecen, Hungary;Department of Statistics, and Center for Stochastic and Chaotic Processes in Science and Technology, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, United States;Department of Electrical and Computer Science, Case Western Reserve University, Cleveland, OH 44106, United States;Department of Pediatric Neurology, Case Western Reserve University, Cleveland, OH 44106, United States;Case Western Reserve University, Cleveland, OH 44106, United States
Venue:
Computer Methods and Programs in Biomedicine
Year:
2009

Citing 1
Cited 4

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

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Abstract

The paper integrates and adapts a range of advanced computational, mathematical and statistical tools for the purpose of analysis of neonate sleep stages based on extensive electroencephalogram (EEG) recordings. The level of brain dysmaturity of a neonate is difficult to assess by direct physical or cognitive examination, but dysmaturity is known to be directly related to the structure of neonatal sleep as reflected in the nonstationary time series produced by EEG signals which, importantly, can be collected trough a noninvasive procedure. In the past, the assessment of sleep EEG structure has often been done manually by experienced clinicians. The goal of this paper is to develop rigorous algorithmic tools for the same purpose by providing a formal scheme to separate different sleep stages corresponding to different stationary segments of the EEG signal based on statistical analysis of the spectral and nonlinear characteristics of the sleep EEG recordings. The methods developed in this paper can, potentially, be translated to other areas of biomedical research.