Seizure detection: evaluation of the Reveal algorithm.
OBJECTIVE
The aim of this study is to evaluate an improved seizure detection algorithm and to compare with two other algorithms and human experts.


METHODS
672 seizures from 426 epilepsy patients were examined with the (new) Reveal algorithm which utilizes 3 methods, novel in their application to seizure detection: Matching Pursuit, small neural network-rules and a new connected-object hierarchical clustering algorithm.


RESULTS
Reveal had a sensitivity of 76% with a false positive rate of 0.11/h. Two other algorithms (Sensa and CNet) were tested and had sensitivities of 35.4 and 48.2% and false positive rates of 0.11/h and 0.75/h, respectively.


CONCLUSIONS
This study validates the Reveal algorithm, and shows it to compare favorably with other methods.


CONCLUSIONS
Improved seizure detection can improve patient care in both the epilepsy monitoring unit and the intensive care unit.
Wilson
Scott B
SB
Persyst Development Corporation, 1060 Sandretto Drive, Suite E2, Prescott, AZ 86305, USA. scottw@eeg-persyst.com
Scheuer
Mark L
ML
Emerson
Ronald G
RG
Gabor
Andrew J
AJ
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D004569:Electroencephalography / Q000706:statistics & numerical data*; D005103:Expert Systems; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D008875:Middle Aged; D018670:Monitoring, Ambulatory; D012372:ROC Curve; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology