Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach

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O'Toole , J M , Boylan , G B , Lloyd , R O , Goulding , R M , Vanhatalo , S & Stevenson , N J 2017 , ' Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach ' , Medical Engineering & Physics , vol. 45 , pp. 42-50 . https://doi.org/10.1016/j.medengphy.2017.04.003

Title: Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach
Author: O'Toole, John M.; Boylan, Geraldine B.; Lloyd, Rhodri O.; Goulding, Robert M.; Vanhatalo, Sampsa; Stevenson, Nathan J.
Other contributor: University of Helsinki, Clinicum



Date: 2017-07
Language: eng
Number of pages: 9
Belongs to series: Medical Engineering & Physics
ISSN: 1350-4533
DOI: https://doi.org/10.1016/j.medengphy.2017.04.003
URI: http://hdl.handle.net/10138/231702
Abstract: Aim: To develop a method that segments preterm EEG into bursts and inter-bursts by extracting and combining multiple EEG features. Methods: Two EEG experts annotated bursts in individual EEG channels for 36 preterm infants with gestational age <30 weeks. The feature set included spectral, amplitude, and frequency-weighted energy features. Using a consensus annotation, feature selection removed redundant features and a support vector machine combined features. Area under the receiver operator characteristic (AUC) and Cohen's kappa (K) evaluated performance within a cross-validation procedure. Results: The proposed channel-independent method improves AUC by 4-5% over existing methods (p <0.001, n = 36), with median (95% confidence interval) AUC of 0.989 (0.973-0.997) and sensitivity -specificity of 95.8-94.4%. Agreement rates between the detector and experts' annotations, K = 0.72 (0.36-0.83) and K = 0.65 (0.32-0.81), are comparable to inter-rater agreement, K = 0.60 (0.21-0.74). Conclusions: Automating the visual identification of bursts in preterm EEG is achievable with a high level of accuracy. Multiple features, combined using a data-driven approach, improves on existing single-feature methods. (C) 2017 The Authors. Published by Elsevier Ltd on behalf of IPEM.
Subject: Burst detection
Electroencephalography
Preterm infant
Feature extraction
Spectral analysis
Support vector machine
Inter-burst interval
PRETERM INFANTS
ACTIVITY TRANSIENTS
RECORDINGS
BORN
ELECTROENCEPHALOGRAM
SUPPRESSION
SELECTION
BRAIN
3111 Biomedicine
3124 Neurology and psychiatry
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