Automated EEG inter-burst interval detection in neonates with mild to moderate postasphyxial encephalopathy.
EEG inter-burst interval (IBI) and its evolution is a robust parameter for grading hypoxic encephalopathy and prognostication in newborns with perinatal asphyxia. We present a reliable algorithm for the automatic detection of IBIs. This automated approach is based on adaptive segmentation of EEG, classification of segments and use of temporal profiles to describe the global distribution of EEG activity. A pediatric neurologist has blindly scored data from 8 newborns with perinatal postasphyxial encephalopathy varying from mild to severe. 15 minutes of EEG have been scored per patient, thus totaling 2 hours of EEG that was used for validation. The algorithm shows good detection accuracy and provides insight into challenging cases that are difficult to detect.
Department of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Belgium. Vladimir.Matic@esat.kuleuven.be
Vladimir
V
Matic
Perumpillichira J
PJ
Cherian
Katrien
K
Jansen
Ninah
N
Koolen
Gunnar
G
Naulaers
Renate M
RM
Swarte
Paul
P
Govaert
Gerhard H
GH
Visser
Sabine
S
Van Huffel
Maarten
M
De Vos
D000465:Algorithms; D001238:Asphyxia Neonatorum / Q000150:complications / Q000175:diagnosis / Q000503:physiopathology; D001927:Brain Diseases / Q000175:diagnosis / Q000209:etiology / Q000503:physiopathology; D004569:Electroencephalography / Q000379:methods*; D001330:Electronic Data Processing / Q000379:methods*; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D012815:Signal Processing, Computer-Assisted