Unsupervised classification of epileptic EEG signals with multi scale K-means algorithm

Zhu, Guohun and Li, Yan and Wen, Peng (Paul) and Wang, Shuaifang and Zhong, Ning (2013) Unsupervised classification of epileptic EEG signals with multi scale K-means algorithm. In: International Conference on Brain and Health Informatics (BHI 2013), 29-31 Oct 2013 , Maebashi, Japan .

Abstract

Most epileptic EEG classification algorithms are supervised and require large training data sets, which hinders its use in real time applications. This paper proposes an unsupervised multi-scale K-means (MSK-means) algorithm to distinguish epileptic EEG signals from normal EEGs. 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 paper, the MSK-means algorithm is proved theoretically being 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 discriminate epileptic EEGs from normal EEGs using six features extracted by the sample entropy technique. The experimental results demonstrate that the MSK-means algorithm achieves 7% higher accuracy with 88% less execution time than that of K-means, and 6% higher accuracy with 97% less execution time than that of the SVM.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © Springer International Publishing Switzerland 2013. 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: 27 Apr 2014 03:22
Last Modified: 20 Feb 2017 06:30
Uncontrolled Keywords: K-means clustering; multi-scale K-means; scale factor
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
11 Medical and Health Sciences > 1109 Neurosciences > 110903 Central Nervous System
17 Psychology and Cognitive Sciences > 1701 Psychology > 170101 Biological Psychology (Neuropsychology, Psychopharmacology, Physiological Psychology)
Socio-Economic Objective: C Society > 92 Health > 9202 Health and Support Services > 920203 Diagnostic Methods
Identification Number or DOI: 10.1007/978-3-319-02753-1_16
URI: http://eprints.usq.edu.au/id/eprint/24454

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