O'Toole JM, Boylan GB, Lloyd RO, Goulding RM, Vanhatalo S, Stevenson NJ. Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach.
Med Eng Phys 2017;
45:42-50. [PMID:
28431822 PMCID:
PMC5461890 DOI:
10.1016/j.medengphy.2017.04.003]
[Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Revised: 03/27/2017] [Accepted: 04/02/2017] [Indexed: 11/22/2022]
Abstract
Machine learning approach enables accurate detection of bursts in preterm EEG.
Features of amplitude and spectral shape capture discriminating information.
Improves reliability of estimates of inter-burst intervals.
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.
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