DocumentCode :
2375254
Title :
Age-independent seizure detection
Author :
Faul, Stephen ; Temko, Andriy ; Marnane, William
Author_Institution :
Dept. of Electr. Eng., Univ. Coll. Cork, Cork, Ireland
fYear :
2009
fDate :
3-6 Sept. 2009
Firstpage :
6612
Lastpage :
6615
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.
Keywords :
electroencephalography; feature extraction; neurophysiology; patient diagnosis; support vector machines; SVM classifier; age independent seizure detection; epilepsy; feature extraction; neonatal data; Adolescent; Adult; Aging; Algorithms; Artifacts; Electroencephalography; Humans; Infant, Newborn; ROC Curve; Seizures; Young Adult;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
Conference_Location :
Minneapolis, MN
ISSN :
1557-170X
Print_ISBN :
978-1-4244-3296-7
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2009.5332553
Filename :
5332553
Link To Document :
بازگشت