Automated detection of neonate EEG sleep stages

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 ph...

Full description

Saved in:
Bibliographic Details
Published in:Computer methods and programs in biomedicine 2009-07, Vol.95 (1), p.31-46
Main Authors: Piryatinska, Alexandra, Terdik, Gyorgy, Woyczynski, Wojbor A, Loparo, Kenneth A, Scher, Mark S, Zlotnik, Anatoly
Format: Article
Language:English
Subjects:
Algorithms
Automatic Data Processing
Automation
Brain - pathology
Brain Mapping - methods
Brain plasticity
Change-point detection
Cluster Analysis
Computational Biology - methods
EEG signals
Electroencephalography - methods
Fractional dimension
Humans
Infant, Newborn
Infant, Premature
Internal Medicine
Models, Statistical
Neonate dysmaturity
Nonlinear
Nonstationary
Other
Signal Processing, Computer-Assisted
Sleep stage separation
Sleep Stages
Spectral analysis
Stationary segments
Time Factors
Time series
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2009.01.006