Automated neonatal seizure detection mimicking a human observer reading EEG.
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.


CONCLUSIONS
The proposed algorithm significantly improves neonatal seizure detection and monitoring.
Deburchgraeve
W
W
Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. wouter.deburchgraeve@esat.kuleuven.be
Cherian
P J
PJ
De Vos
M
M
Swarte
R M
RM
Blok
J H
JH
Visser
G H
GH
Govaert
P
P
Van Huffel
S
S
D000465:Algorithms; D016022:Case-Control Studies; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007232:Infant, Newborn, Diseases / Q000175:diagnosis*; D011237:Predictive Value of Tests; D012640:Seizures / Q000145:classification / Q000175:diagnosis*; D012680:Sensitivity and Specificity