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Hoffmann, R. A wiki for the life sciences where authorship matters. Nature Genetics (2008)

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.[1]


  1. Seizure detection: evaluation of the Reveal algorithm. Wilson, S.B., Scheuer, M.L., Emerson, R.G., Gabor, A.J. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. (2004) [Pubmed]
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