Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach
O'Toole, John M.
Boylan, Geraldine B.
Lloyd, Rhodri O.
Goulding, Robert M.
Stevenson, Nathan J.
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 (κ) 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, κ=0.72 (0.36–0.83) and κ=0.65 (0.32–0.81), are comparable to inter-rater agreement, κ=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.
Burst detection , Electroencephalography , Preterm infant , Feature extraction , Spectral analysis , Support vector machine , Inter-burst interval
O’Toole, J. M., Boylan, G. B., Lloyd, R. O., Goulding, R. M., Vanhatalo, S. and Stevenson, N. J. (2017) 'Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach', Medical Engineering & Physics, 45, pp. 42-50. doi:10.1016/j.medengphy.2017.04.003