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Updated: Jun 30, 2026
Application of an Amplitude-integrated EEG Monitor (Cerebral Function Monitor) to Neonates
Published on: September 6, 2017
Automated neonatal seizure detection mimicking a human observer reading EEG
W Deburchgraeve1, P J Cherian, M De Vos
1Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. wouter.deburchgraeve@esat.kuleuven.be
Objective:
The description and evaluation of a novel patient-independent seizure detection for the EEG of the newborn term infant.
Methods:
We identified characteristics of neonatal seizures by which a human observer is able to detect them. Neonatal seizures were divided into two types. For each type, a fully automated detection algorithm was developed based on the identified human observer characteristics. The first algorithm analyzes the correlation between high-energetic segments of the EEG. The second detects increases in low-frequency activity (<8 Hz) with high autocorrelation.
Results:
The complete algorithm was tested on multi-channel EEG recordings of 21 patients with and 5 patients without electrographic seizures, totaling 217 h of EEG. Sensitivity of the combined algorithms was found to be 88%, Positive Predictive Value (PPV) 75% and the false positive rate 0.66 per hour.
Conclusions:
Our approach to separate neonatal seizures into two types yields a high sensitivity combined with a good PPV and much lower false positive rate than previously published algorithms.
Significance:
The proposed algorithm significantly improves neonatal seizure detection and monitoring.
