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

Text ((Front Matter))
Signal and Image Analysis.pdf Restricted |

## 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) |
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Refereed: | Yes |

Item Status: | Live Archive |

Additional Information: | Permanent restricted access to published version due to publisher copyright policy. |

Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |

Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |

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 (2008): | 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 |

Fields of Research (2020): | 40 ENGINEERING > 4006 Communications engineering > 400607 Signal processing 49 MATHEMATICAL SCIENCES > 4904 Pure mathematics > 490403 Category theory, k theory, homological algebra 49 MATHEMATICAL SCIENCES > 4901 Applied mathematics > 490102 Biological mathematics |

Socio-Economic Objectives (2008): | E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences |

Identification Number or DOI: | https://doi.org/10.1007/978-3-319-10984-8_8 |

URI: | http://eprints.usq.edu.au/id/eprint/26716 |

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