Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection-Reference-Cited by-同舟云学术

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Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection

Published:2016-03-31 Issue:8 Volume:54 Page:1285-1293
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Bogaarts J. G.,Gommer E. D.,Hilkman D. M. W.,van Kranen-Mastenbroek V. H. J. M.,Reulen J. P. H.

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

http://link.springer.com/content/pdf/10.1007/s11517-016-1468-y.pdf

Reference30 articles.

1. Aarabi A, Wallois F, Grebe R (2006) Automated neonatal seizure detection: a multistage classification system through feature selection based on relevance and redundancy analysis. Clin Neurophysiol 117:328–340

2. Aarabi A, Grebe R, Wallois F (2007) A multistage knowledge-based system for EEG seizure detection in newborn infants. Clin Neurophysiol 118:2781–2797

3. Bogaarts JG, Gommer ED, Hilkman DM, van Kranen-Mastenbroek VH, Reulen JP (2014) EEG feature pre-processing for neonatal epileptic seizure detection. Ann Biomed Eng 42:2360–2368

4. Boylan GB, Stevenson NJ, Vanhatalo S (2013) Monitoring neonatal seizures. Semin Fetal Neonatal Med 18:202–208

5. Claassen J, Mayer SA, Kowalski RG, Emerson RG, Hirsch LJ (2004) Detection of electrographic seizures with continuous EEG monitoring in critically ill patients. Neurology 62:1743–1748

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