Literature DB >> 15351370

Seizure detection: evaluation of the Reveal algorithm.

Scott B Wilson1, Mark L Scheuer, Ronald G Emerson, Andrew J Gabor.   

Abstract

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.

Entities:  

Mesh:

Year:  2004        PMID: 15351370     DOI: 10.1016/j.clinph.2004.05.018

Source DB:  PubMed          Journal:  Clin Neurophysiol        ISSN: 1388-2457            Impact factor:   3.708


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