Electroencephalogram (EEG) is a gold standard in epilepsy diagnosis and has been widely studied for epilepsy-related signal classification. In the past few years, discrete wavelet transform (DWT) has been widely used to analyze epileptic EEG. However, there are two practical questions unanswered: 1. what the best mother wavelet for epileptic EEG analysis is; 2. what the optimal level of wavelet decomposition is. The main challenge in using wavelet transform is selecting the optimal mother wavelet for the given task, as different mother wavelet applied on the same signal may produces different results. Such a problem also exist in epileptic EEG analysis based on wavelet. Deeper DWT can yield more detailed depiction of signals but it requires substantially more computational time. In this paper, we study these problems, using the most common epileptic EEG classification task, seizure detection, as an example. The results show that all 7 mother wavelets used in this work achieve high seizure detection accuracy at high decomposition levels. Also, decomposition level effects the detection accuracy more significantly than mother wavelets. For all wavelets, decomposition beyond level 7 improves accuracy limitedly and even decreases accuracy. We further study the most effective bands and features for seizure detection. An interpretation to our results is that seizure and non-seizure EEGs differ across all conventional frequency bands of human EEG rhythms. The best accuracy of seizure detection achieved in this research is 92.30% using coif3 from levels 2 to 7.
['Duo Chen', 'Suiren Wan', 'Forrest Sheng Bao']
EEG-based seizure detection using discrete wavelet transform through full-level decomposition