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. Significance: Improved seizure detection can improve patient care in both the epilepsy monitoring unit and the intensive care unit. (C) 2004 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.