An analysis system detecting epileptic seizure from EEG

Kabir, Enamul and Siuly, and Wang, Hua (2017) An analysis system detecting epileptic seizure from EEG. In: 2017 Young Statisticians Conference: Modelling Our Future , 26-27 Sept 2017, Tweed Heads, Australia.

[img]
Preview
Text (Published Abstract)
Abstract.pdf

Download (8Kb) | Preview
Official URL: http://ysc2017.com.au/

Abstract

This paper presents an analysis system for detecting epileptic seizure from electroencephalogram (EEG). As EEG recordings contain a vast amount of data, which is heterogeneous with respect to a time-period, we intend to introduce a clustering technique to discover different groups of data according to similarities or dissimilarities among the patterns. In the proposed methodology, we use K-means clustering for partitioning each category EEG data set (e.g. healthy; epileptic seizure) into several clusters and then extract some representative characteristics from each cluster. Subsequently, we integrate all the features from all the clusters in one feature set and then evaluate that feature set by three well-known machine learning methods: Support Vector Machine (SVM), Naive bayes and Logistic regression. The proposed method is tested by a publicly available benchmark database: 'Epileptic EEG database'. The experimental results show that the proposed scheme with SVM classifier yields overall accuracy of 100% for classifying healthy vs epileptic seizure signals and outperforms all the recent reported existing methods in the literature. The major finding of this research is that the proposed K-means clustering based approach has an ability to efficiently handle EEG data for the detection of epileptic seizure.


Statistics for USQ ePrint 33578
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Speech)
Refereed: Yes
Item Status: Live Archive
Additional Information: Oral presentation - Abstract only published in Proceedings. No evidence of copyright restrictions preventing deposit of Published Abstract.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 16 Jan 2018 04:44
Last Modified: 18 Jan 2018 01:08
Uncontrolled Keywords: EEG, clustering, SVM
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
URI: http://eprints.usq.edu.au/id/eprint/33578

Actions (login required)

View Item Archive Repository Staff Only