Epileptic Focus Localization Using EEG Based On Discrete Wavelet Transform Through Full-Level Decomposition
Duo Chen, Suiren Wang, Forrest Sheng Bao

Abstract:
Electroencephalogram (EEG) is a gold standard in epilepsy diagnosis and has been widely studied for epilepsy-related signal classification, such as seizure detection or focus localization. In the past few years, discrete wavelet transform (DWT) has been widely used to analyze epileptic EEG. However, one practical question unanswered is the optimal levels of wavelet decomposition. Deeper DWT can yield a more detailed depiction of signals but it requires substantially more computational time. In this paper, we study this problem, using the most difficult epileptic EEG classification task, focus localization, as an example. The results show that decomposition level effects the localization accuracy more significantly than mother wavelets. For all wavelets, decomposition beyond level 7 improves accuracy limitedly and even decreases accuracy. We further study what are the most effective bands and features for focus localization. An interpretation of our results is that focal and non-focal epileptic EEGs differ the most at high frequencies of EEG rhythms. The best accuracy of epileptic focus localization achieved in this research is 83.07\% using sym6 from levels 1 to 7.