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

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.