Literature DB >> 19963678

Age-independent seizure detection.

Stephen Faul1, Andriy Temko, William Marnane.   

Abstract

This paper examines whether an appropriate algorithm, developed for use with neonatal data, could also be used, without alteration, for the detection of seizures in adults with epilepsy. The performance of a feature extraction and SVM classifier system is evaluated on databases of 17 neonatal patients and 15 adult patients. Mean ROC curve areas of 0.96 and 0.94 for neonatal and adult databases respectively show that high accuracy can be achieved independent of age. It is also shown that features contribute differently for neonatal and adult data.

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Year:  2009        PMID: 19963678     DOI: 10.1109/IEMBS.2009.5332553

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  5 in total

1.  Optimal features for online seizure detection.

Authors:  Lojini Logesparan; Alexander J Casson; Esther Rodriguez-Villegas
Journal:  Med Biol Eng Comput       Date:  2012-04-03       Impact factor: 2.602

2.  Robust neonatal EEG seizure detection through adaptive background modeling.

Authors:  Andriy Temko; Geraldine Boylan; William Marnane; Gordon Lightbody
Journal:  Int J Neural Syst       Date:  2013-06-04       Impact factor: 5.866

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

Authors:  J G Bogaarts; E D Gommer; D M W Hilkman; V H J M van Kranen-Mastenbroek; J P H Reulen
Journal:  Med Biol Eng Comput       Date:  2016-03-31       Impact factor: 2.602

4.  Performance assessment for EEG-based neonatal seizure detectors.

Authors:  A Temko; E Thomas; W Marnane; G Lightbody; G B Boylan
Journal:  Clin Neurophysiol       Date:  2010-08-15       Impact factor: 3.708

5.  Ultra low power signal oriented approach for wireless health monitoring.

Authors:  Stevan Marinkovic; Emanuel Popovici
Journal:  Sensors (Basel)       Date:  2012-06-08       Impact factor: 3.576

  5 in total

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