Peer-Reviewed Journal Details
Mandatory Fields
O’Toole, John M.; Boylan, Geraldine B.; Lloyd, Rhodri O.; Goulding, Robert M.; Vanhatalo, Sampsa; Stevenson, Nathan J.
2017
July
Medical Engineering & Physics
Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach
Published
WOS: 15 ()
Optional Fields
Burst detection Electroencephalography Preterm infant Feature extraction Spectral analysis Support vector machine Inter-burst interval
45
42
50
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 (¿) 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, ¿ = 0.72 (0.36–0.83) and ¿ = 0.65 (0.32–0.81), are comparable to inter-rater agreement, ¿ = 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.
1350-4533
10.1016/j.medengphy.2017.04.003
Grant Details