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
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;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
Conference_Location :
Florence
DOI :
10.1109/ICASSP.2014.6854734