Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel. (1st March 2017)

• Record Type: Journal Article
• Authors: Ahmed, Rehan; Temko, Andriy; Marnane, William P.; Boylan, Geraldine; Lightbody, Gordon
• Is Part Of: Computers in biology and medicine. Volume 82(2017)
• Page Start: 100
• Page End: 110
• Publication Date: 2017-03-01
• Subjects: Automated neonatal seizure detection -- Sequential classifier -- Fusion -- Gaussian dynamic time warping; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.01.017
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Seizure events in newborns change in frequency, morphology, and propagation. This contextual information is explored at the classifier level in the proposed patient-independent neonatal seizure detection system. The system is based on the combination of a static and a sequential SVM classifier. A Gaussian dynamic time warping based kernel is used in the sequential classifier. The system is validated on a large dataset of EEG recordings from 17 neonates. The obtained results show an increase in the detection rate at very low false detections per hour, particularly achieving a 12% improvement in the detection of short seizure events over the static RBF kernel based system.