Unsupervised classification of epileptic EEG signals with multi scale K-means algorithm
Paper
Paper/Presentation Title | Unsupervised classification of epileptic EEG signals with multi scale K-means algorithm |
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Presentation Type | Paper |
Authors | Zhu, Guohun (Author), Li, Yan (Author), Wen, Peng (Paul) (Author), Wang, Shuaifang (Author) and Zhong, Ning (Author) |
Editors | Imamura, Kazayuki, Usui, Shiro, Shirao, Tomoaki, Kasamatsu, Takuji, Schwabe, Lars and Zhong, Ning |
Journal or Proceedings Title | Lecture Notes in Computer Science (Book series) |
Journal Citation | 8211, pp. 158-167 |
Number of Pages | 10 |
Year | 2013 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1611-3349 |
0302-9743 | |
ISBN | 9783319027524 |
9783319027531 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-319-02753-1_16 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-319-02753-1_16 |
Web Address (URL) of Conference Proceedings | http://wi-consortium.org/conferences/amtbi13/bhi/index.php |
Conference/Event | 2013 International Conference on Brain and Health Informatics (BHI 2013) |
Event Details | 2013 International Conference on Brain and Health Informatics (BHI 2013) Parent International Conference on Brain and Health Informatics Event Date 29 to end of 31 Oct 2013 Event Location 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. |
Keywords | K-means clustering; multi-scale K-means; scale factor |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
320903. Central nervous system | |
520299. Biological psychology not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Centre for Systems Biology |
School of Mechanical and Electrical Engineering | |
Faculty of Health, Engineering and Sciences | |
Maebashi Institute of Technology, Japan | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q2303/unsupervised-classification-of-epileptic-eeg-signals-with-multi-scale-k-means-algorithm
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