Automatic seizure detection incorporating structural information

Authors:
Borbala Hunyadi;Maarten De Vos;Marco Signoretto;Johan A. K. Suykens;Wim Van Paesschen;Sabine Van Huffel
Affiliations:
Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium and IBBT, K.U. Leuven Future Health Department, Leuven, Belgium;Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium and IBBT, K.U. Leuven Future Health Department, Leuven, Belgium and Neuropsychology Lab, Department of Psychol ...;Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium and IBBT, K.U. Leuven Future Health Department, Leuven, Belgium;Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium and IBBT, K.U. Leuven Future Health Department, Leuven, Belgium;Department of Neurology, University Hospital Gasthuisberg, Leuven, Belgium;Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium and IBBT, K.U. Leuven Future Health Department, Leuven, Belgium
Venue:
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Year:
2011

Citing 3
Cited 0

Least Squares Support Vector Machine Classifiers

Neural Processing Letters
Supervised tensor learning

Knowledge and Information Systems
Coupled simulated annealing

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Traditional seizure detection algorithms act on single channels ignoring the synchronously recorded, inherently interdependent multichannel nature of EEG. However, the spatial distribution and evolution of the ictal pattern is a crucial characteristic of the seizure. Two different approaches aiming at including such structural information into the data representation are presented in this paper. Their performance is compared to the traditional approach both in a simulation study and a real-life example, showing that spatial and structural information facilitates precise classification.