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 (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.
Highlights
Preterm birth is the single largest risk factor for perinatal mortality and morbidity, accounting for over 1 million deaths every year [1]
The proposed channel-independent method improves Area under the receiver operator characteristic (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
EEG data were collected from the neonatal intensive care unit (NICU) of the Cork University Maternity Hospital, Ireland, during the period 2009–2011
Summary
Preterm birth is the single largest risk factor for perinatal mortality and morbidity, accounting for over 1 million deaths every year [1]. The immature brain of the preterm infant is especially vulnerable and often the source of long-term health problems. The electroencephalogram (EEG) can help identify at-risk infants by providing continuous cot-side monitoring of brain activity in the neonatal intensive care unit (NICU). The EEG, requires interpretation by specialist staff which often makes it impractical to provide continuous reporting for all infants. Automated EEG analysis could overcome this limitation and provide the clinician with relevant information, in real time, to guide treatment during critical care. Preterm EEG exhibits an intermittent or discontinuous pattern (tracé discontinu) consisting of low-voltage activity, known
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