DocumentCode
179713
Title
Grading brain injury in neonatal EEG using SVM and supervector kernel
Author
Ahmed, Rizwan ; Temko, Andriy ; Marnane, William ; Boylan, Geraldine ; Lightbody, G.
Author_Institution
Neonatal Brain Res. Group, Irish Center for Fetal & Neonatal Translational Res., Ireland
fYear
2014
fDate
4-9 May 2014
Firstpage
5894
Lastpage
5898
Abstract
Brain injury at the time of birth could lead to severe neurological dysfunction at an older age. Grading the brain injury in the early hours after birth could help doctors determine a prompt and reliable treatment. This work presents an automated neonatal EEG grading system based on a cross-disciplinary method of using Support Vector Machine and supervectors, initially developed for speaker identification. The EEG is classified into one of the four grades of neonatal brain injury. The preliminary results show promising performance and are an improvement on the previously published results.
Keywords
electroencephalography; neurophysiology; support vector machines; automated neonatal EEG grading system; cross-disciplinary method; neonatal brain injury; neurological dysfunction; supervector kernel; support vector machine; Brain models; Electroencephalography; Feature extraction; Pediatrics; Support vector machines; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
Conference_Location
Florence
Type
conf
DOI
10.1109/ICASSP.2014.6854734
Filename
6854734
Link To Document