Classifying epileptic EEG signals with delay permutation entropy and multi-scale K-means
Edited book (chapter)
| Chapter Title | Classifying epileptic EEG signals with delay permutation entropy and multi-scale K-means |
|---|---|
| Book Chapter Category | Edited book (chapter) |
| ERA Publisher ID | 3337 |
| Book Title | Signal and image analysis for biomedical and life sciences |
| Authors | Zhu, Guohun (Author), Li, Yan (Author), Wen, Peng (Paul) (Author) and Wang, Shuaifang (Author) |
| Editors | Sun, Changming, Bednarz, Tomasz, Pham, Tuan D., Vallotton, Pascal and Wang, Dadong |
| Volume | 823 |
| Page Range | 143-157 |
| Series | Advances in Experimental Medicine and Biology |
| Chapter Number | 8 |
| Number of Pages | 15 |
| Year | 2015 |
| Publisher | Springer |
| Place of Publication | United States |
| ISBN | 9783319109831 |
| 9783319109848 | |
| ISSN | 0065-2598 |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-319-10984-8_8 |
| Web Address (URL) | https://link.springer.com/chapter/10.1007/978-3-319-10984-8_8 |
| 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. |
| Keywords | delay permutation entropy; epileptogenic focus location; MSK-means; seizure detection; SVM; unsupervised learning |
| ANZSRC Field of Research 2020 | 400607. Signal processing |
| 490403. Category theory, k theory, homological algebra | |
| 490102. Biological mathematics | |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Journal Title | Advances in Experimental Medicine and Biology |
| Institution of Origin | University of Southern Queensland |
| Byline Affiliations | University of Southern Queensland |
| Guilin University of Electronic Technology, China |
https://research.usq.edu.au/item/q2x2v/classifying-epileptic-eeg-signals-with-delay-permutation-entropy-and-multi-scale-k-means
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