Most epileptic EEG classification algorithms are supervised and require large training datasets, that hinder their use in real time applications. This chapter proposes an unsupervised Multi-Scale K-means (MSK-means) algorithm to distinguish epileptic EEG signals and identify epileptic zones. The random initialization of the K-means algorithm can lead to wrong clusters. Based on the characteristics of EEGs, the MSK-means algorithm initializes the coarse-scale centroid of a cluster with a suitable scale factor. In this chapter, the MSK-means algorithm is proved theoretically superior to the K-means algorithm on efficiency. In addition, three classifiers: the K-means, MSK-means and support vector machine (SVM), are used to identify seizure and localize epileptogenic zone using delay permutation entropy features. The experimental results demonstrate that identifying seizure with the MSK-means algorithm and delay permutation entropy achieves 4.7% higher accuracy than that of K-means, and 0.7% higher accuracy than that of the SVM.
CITATION STYLE
Zhu, G., Li, Y., Wen, P. P., & Wang, S. (2015). Classifying epileptic EEG signals with delay permutation entropy and multi-scale K-means. Advances in Experimental Medicine and Biology, 823, 143–157. https://doi.org/10.1007/978-3-319-10984-8_8
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