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Signal and Image Analysis for Biomedical and Life Sciences pp 143–157Cite as

Classifying Epileptic EEG Signals with Delay Permutation Entropy and Multi-scale K-Means

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Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 823))

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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Author information

Authors and Affiliations

  1. Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD, 4350, Australia

    Guohun Zhu, Yan Li, Peng (Paul) Wen & Shuaifang Wang

  2. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, Guangxi, 541004, China

    Guohun Zhu

Authors
  1. Guohun Zhu
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  2. Yan Li
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  3. Peng (Paul) Wen
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  4. Shuaifang Wang
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Corresponding author

Correspondence to Guohun Zhu .

Editor information

Editors and Affiliations

  1. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Changming Sun

  2. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Tomasz Bednarz

  3. The University of Aizu, Fukushima, Japan

    Tuan D. Pham

  4. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Pascal Vallotton

  5. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Dadong Wang

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Zhu, G., Li, Y., Wen, P.(., Wang, S. (2015). Classifying Epileptic EEG Signals with Delay Permutation Entropy and Multi-scale K-Means. In: Sun, C., Bednarz, T., Pham, T., Vallotton, P., Wang, D. (eds) Signal and Image Analysis for Biomedical and Life Sciences. Advances in Experimental Medicine and Biology, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-319-10984-8_8

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