Classifying epileptic EEG signals with delay permutation entropy and Multi-Scale K-means.
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) 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 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 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.
Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD, 4350, Australia, guohun.zhu@usq.edu.au.
Guohun
G
Zhu
Yan
Y
Li
Peng Paul
PP
Wen
Shuaifang
S
Wang
D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D004827:Epilepsy / Q000175:diagnosis / Q000503:physiopathology*; D006801:Humans; D008959:Models, Neurological; D012640:Seizures / Q000175:diagnosis / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine