Seizure detection algorithm for neonates based on wave-sequence analysis.
OBJECTIVE
The description and evaluation of the performance of a new real-time seizure detection algorithm in the newborn infant.


METHODS
The algorithm includes parallel fragmentation of EEG signal into waves; wave-feature extraction and averaging; elementary, preliminary and final detection. The algorithm detects EEG waves with heightened regularity, using wave intervals, amplitudes and shapes. The performance of the algorithm was assessed with the use of event-based and liberal and conservative time-based approaches and compared with the performance of Gotman's and Liu's algorithms.


RESULTS
The algorithm was assessed on multi-channel EEG records of 55 neonates including 17 with seizures. The algorithm showed sensitivities ranging 83-95% with positive predictive values (PPV) 48-77%. There were 2.0 false positive detections per hour. In comparison, Gotman's algorithm (with 30s gap-closing procedure) displayed sensitivities of 45-88% and PPV 29-56%; with 7.4 false positives per hour and Liu's algorithm displayed sensitivities of 96-99%, and PPV 10-25%; with 15.7 false positives per hour.


CONCLUSIONS
The wave-sequence analysis based algorithm displayed higher sensitivity, higher PPV and a substantially lower level of false positives than two previously published algorithms.


CONCLUSIONS
The proposed algorithm provides a basis for major improvements in neonatal seizure detection and monitoring.
Navakatikyan
Michael A
MA
BrainZ Instruments Ltd, 25 Carbine Road, Mt Wellington, P.O. Box 51078 Pakuranga, Auckland 1730, New Zealand. michael.navakatikyan@brainz.com
Colditz
Paul B
PB
Burke
Chris J
CJ
Inder
Terrie E
TE
Richmond
Jane
J
Williams
Christopher E
CE
D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D005189:False Positive Reactions; D006801:Humans; D007231:Infant, Newborn; D007232:Infant, Newborn, Diseases / Q000175:diagnosis*; D008959:Models, Neurological; D012680:Sensitivity and Specificity