Abstract
Abstract Visual inspection of Electroencephalogram (EEG) records is the conventional diagnostic method of epilepsy but it is expensive, time-consuming and tedious. Therefore, it is necessary to develop automated seizure detection technologies. In this paper, a new entropy named fuzzy distribution entropy (fDistEn) was first put forward and then a seizure detection scheme combining wavelet packet decomposition (WPD), fDistEn, Kruskal-Wallis nonparametric one-way analysis of variance and k -nearest neighbor ( k -NN) classifier was proposed. In the proposed scheme, WPD was firstly implemented to decompose the filtered EEG into several wavelet sub-bands. Subsequently, fDistEn values of all nodes in every level were calculated and followed by selecting significant features using Kruskal-Wallis test. Finally, k -NN was employed to classify ten kinds of EEG combinations. Experimental results show fDistEn can measure the complexity of signals and our proposed scheme is qualified to detect seizure automatically with not less than 98.338% accuracy in all cases. Compared with existing methods, our scheme outperforms most of state-of-the-art articles and it indicates the effectiveness of the proposed seizure detection scheme.
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