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The performance of three Entropy/complexity measures in detecting EEG seizures in the neonate were investigated in this study. A dataset containing EEG recordings from 11 neonates, with documented electrographic seizures, was employed in this study. Based on patient independent tests Shannon Entropy was found to provide the best in discrimination between seizure and non-seizure EEG in the neonate. Lempel-Ziv complexity and Multi-scale Entropy were second and third respectively, while Sample Entropy did not prove a useful feature for discriminating seizure patterns from non-seizure patterns.

INVESTIGATION OF ENTROPY AND COMPLEXITY MEASURES FOR DETECTION OF SEIZURES IN THE NEONATE