Seizure detection: evaluation of the Reveal algorithm

• Published in: Clinical neurophysiology Vol. 115; no. 10; pp. 2280 - 2291
• Main Authors: Wilson, Scott B., Scheuer, Mark L., Emerson, Ronald G., Gabor, Andrew J.
• Format: Journal Article
• Language: English
• Published: Shannon Elsevier Ireland Ltd 01.10.2004; Elsevier Science
• Subjects: Adolescent; Adult; Algorithm; Algorithms; Biological and medical sciences; Child; Child, Preschool; Cluster Analysis; Clustering; Electrodiagnosis. Electric activity recording; Electroencephalography; Electroencephalography - statistics & numerical data; Expert Systems; False Positive Reactions; Female; Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy; Humans; Infant; Investigative techniques, diagnostic techniques (general aspects); Male; Matching pursuit; Medical sciences; Middle Aged; Monitoring, Ambulatory; Nervous system; Nervous system (semeiology, syndromes); Neural network; Neurology; ROC Curve; Seizure detection; Seizures - diagnosis; Seizures - physiopathology; Electroencephalography; Neural network; Seizure detection; Clustering; Algorithm; Matching pursuit; Cluster analysis; Human; Evaluation; Nervous system diseases; Epilepsy; Epileptic seizure; Intensive care unit; Cerebral disorder; Electrodiagnosis; Central nervous system disease; Signal detection; Monitoring
• ISSN: 1388-2457; 1872-8952
• DOI: 10.1016/j.clinph.2004.05.018

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. Significance: Improved seizure detection can improve patient care in both the epilepsy monitoring unit and the intensive care unit.