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.

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doi.org/10.1016/j.clinph.2008.07.281, hdl.handle.net/1765/14522
Clinical Neurophysiology
Erasmus MC: University Medical Center Rotterdam

Deburchgraeve, W., Cherian, J., de Vos, M., Swarte, R., Blok, J., Visser, G. H., … Van Huffel, S. (2008). Automated neonatal seizure detection mimicking a human observer reading EEG. Clinical Neurophysiology, 119(11), 2447–2454. doi:10.1016/j.clinph.2008.07.281