Your browser doesn't support javascript.
loading
Classifying epileptic EEG signals with delay permutation entropy and Multi-Scale K-means.
Zhu, Guohun; Li, Yan; Wen, Peng Paul; Wang, Shuaifang.
Afiliación
  • Zhu G; Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD, 4350, Australia, guohun.zhu@usq.edu.au.
Adv Exp Med Biol ; 823: 143-57, 2015.
Article en En | MEDLINE | ID: mdl-25381106
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) 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 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 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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Electroencefalografía / Epilepsia / Modelos Neurológicos Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Adv Exp Med Biol Año: 2015 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Electroencefalografía / Epilepsia / Modelos Neurológicos Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Adv Exp Med Biol Año: 2015 Tipo del documento: Article