On the Selection of Time-Frequency Features for Improving the Detection and Classification of Newborn EEG Seizure Signals and Other Abnormalities
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Publisher
Springer Berlin Heidelberg
Link
http://link.springer.com/content/pdf/10.1007/978-3-642-34478-7_77.pdf
Reference14 articles.
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3. Löfhede, J., et al.: Automatic classification of background EEG activity in healthy and sick neonates. Journal of Neural Engineering 7(1), 016007 (2010)
4. Greene, B.R., et al.: A comparison of quantitative EEG features for neonatal seizure detection. Journal of Clinical Neurophysiology 119(6), 1248–1261 (2008)
5. Mitra, J., Glover, J.R., Ktonas, P.Y., et al.: A multistage system for the automated detection of Epileptic seizures in neonatale Electroencephalography. Journal of Clinical Neurophysiology 26(4), 218–226 (2006)
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