Classifying epileptic EEG signals with delay permutation entropy and multi-scale K-means

Zhu, Guohun and Li, Yan and Wen, Peng (Paul) and Wang, Shuaifang (2015) Classifying epileptic EEG signals with delay permutation entropy and multi-scale K-means. In: Signal and image analysis for biomedical and life sciences. Advances in Experimental Medicine and Biology, 823 (1). Springer, New York, NY. United States, pp. 143-157. ISBN 978-3-319-10983-1

Abstract

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.


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Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to published version due to publisher copyright policy.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 10 Feb 2015 06:27
Last Modified: 29 Nov 2017 00:38
Uncontrolled Keywords: delay permutation entropy; epileptogenic focus location; MSK-means; seizure detection; SVM; unsupervised learning
Fields of Research : 09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
01 Mathematical Sciences > 0101 Pure Mathematics > 010103 Category Theory, K Theory, Homological Algebra
01 Mathematical Sciences > 0102 Applied Mathematics > 010202 Biological Mathematics
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences
Identification Number or DOI: 10.1007/978-3-319-10984-8_8
URI: http://eprints.usq.edu.au/id/eprint/26716

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