TY - JOUR
T1 - Automatic burst detection for the EEG of the preterm infant
AU - Jennekens, Ward
AU - Ruijs, Loes S.
AU - Lommen, Charlotte M. L.
AU - Niemarkt, Hendrik J.
AU - Pasman, Jaco W.
AU - van Kranen-Mastenbroek, Vivianne H. J. M.
AU - Wijn, Pieter F. F.
AU - van Pul, Carola
AU - Andriessen, Peter
PY - 2011/10
Y1 - 2011/10
N2 - To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological follow-up at 2 years. The detection algorithm (MATLABR (R)) for burst, IBI and continuous pattern is based on selection by amplitude, time span, number of channels and numbers of active electrodes. Annotations of two neurophysiologists were used to determine threshold values. The training set consisted of EEG recordings of four preterm infants with postmenstrual age (PMA, gestational age + postnatal age) of 29-34 weeks. Optimal threshold values were based on overall highest sensitivity. For evaluation, both observers verified detections in an independent dataset of four EEG recordings with comparable PMA. Algorithm performance was assessed by calculation of sensitivity and positive predictive value. The results of algorithm evaluation are as follows: sensitivity values of 90% +/- 6%, 80% +/- 9% and 97% +/- 5% for burst, IBI and continuous patterns, respectively. Corresponding positive predictive values were 88% +/- 8%, 96% +/- 3% and 85% +/- 15%, respectively. In conclusion, the algorithm showed high sensitivity and positive predictive values for bursts, IBIs and continuous patterns in preterm EEG. Computer-assisted analysis of EEG may allow objective and reproducible analysis for clinical treatment.
AB - To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological follow-up at 2 years. The detection algorithm (MATLABR (R)) for burst, IBI and continuous pattern is based on selection by amplitude, time span, number of channels and numbers of active electrodes. Annotations of two neurophysiologists were used to determine threshold values. The training set consisted of EEG recordings of four preterm infants with postmenstrual age (PMA, gestational age + postnatal age) of 29-34 weeks. Optimal threshold values were based on overall highest sensitivity. For evaluation, both observers verified detections in an independent dataset of four EEG recordings with comparable PMA. Algorithm performance was assessed by calculation of sensitivity and positive predictive value. The results of algorithm evaluation are as follows: sensitivity values of 90% +/- 6%, 80% +/- 9% and 97% +/- 5% for burst, IBI and continuous patterns, respectively. Corresponding positive predictive values were 88% +/- 8%, 96% +/- 3% and 85% +/- 15%, respectively. In conclusion, the algorithm showed high sensitivity and positive predictive values for bursts, IBIs and continuous patterns in preterm EEG. Computer-assisted analysis of EEG may allow objective and reproducible analysis for clinical treatment.
KW - EEG
KW - computer-assisted signal processing
KW - premature infant
U2 - 10.1088/0967-3334/32/10/010
DO - 10.1088/0967-3334/32/10/010
M3 - Article
C2 - 21896968
SN - 0967-3334
VL - 32
SP - 1623
EP - 1637
JO - Physiological Measurement
JF - Physiological Measurement
IS - 10
ER -