Open AccessFast SVM‐based epileptic seizure prediction employing data prefetching
Author(s) -
Lim Chungsoo,
Nam Sang Won,
Chang JoonHyuk
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2012.3414
Subject(s) - support vector machine , computer science , epileptic seizure , artificial intelligence , pattern recognition (psychology) , machine learning , data mining , epilepsy , neuroscience , biology
To achieve high prediction accuracy for epileptic seizure prediction, a support vector machine (SVM) has been adopted due to its robust classification performance. However, in order to use an SVM for real‐time applications such as seizure prediction, the slow classification speed of an SVM should be addressed. For this purpose, data prefetching that enhances the classification speed of an SVM by mitigating the gap between the processor and the main memory is employed.