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.

Algorithm, Electroencephalography (EEG), Newborn, Seizure detection, algorithm, article, automation, clinical article, controlled study, correlation analysis, diagnostic procedure, diagnostic value, electroencephalogram, electrography, false positive result, human, monitoring, newborn, observer variation, priority journal, seizure, sensitivity analysis
dx.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, P.J, de Vos, M, Swarte, R.M.C, Blok, J.H, 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