Ahmed, R;Temko, A;Marnane, W;Lightbody, G;Boylan, G

2016

January

Clinical Neurophysiology

Grading hypoxic-ischemic encephalopathy severity in neonatal EEG using GMM supervectors and the support vector machine

Validated

WOS: 30 ()

GAUSSIAN MIXTURE-MODELS SEIZURE DETECTION CLASSIFICATION PREDICTION ALGORITHM OUTCOMES

127

297

309

Objective: This work presents a novel automated system to classify the severity of hypoxic-ischemic encephalopathy (HIE) in neonates using EEG. Methods: A cross disciplinary method is applied that uses the sequences of short-term features of EEG to grade an hour long recording. Novel post-processing techniques are proposed based on majority voting and probabilistic methods. The proposed system is validated with one-hour-long EEG recordings from 54 full term neonates. Results: An overall accuracy of 87% is achieved. The developed grading system has improved both the accuracy and the confidence/quality of the produced decision. With a new label 'unknown' assigned to the recordings with lower confidence levels an accuracy of 96% is attained. Conclusion: The statistical long-term model based features extracted from the sequences of short-term features has improved the overall accuracy of grading the HIE injury in neonatal EEG. Significance: The proposed automated HIE grading system can provide significant assistance to health-care professionals in assessing the severity of HIE. This represents a practical and user friendly implementation which acts as a decision support system in the clinical environment. Its integration with other EEG analysis algorithms may improve neonatal neurocritical care. (C) 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

CLARE

1388-2457

10.1016/j.clinph.2015.05.024