Kabir, Enamul 
ORCID: https://orcid.org/0000-0002-6157-2753 and Siuly ORCID: https://orcid.org/0000-0003-2491-0546 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.
|
Text (Published Abstract)
Abstract.pdf Download (8kB) | Preview |
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 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/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Date Deposited: | 16 Jan 2018 04:44 |
| Last Modified: | 23 May 2018 01:46 |
| Uncontrolled Keywords: | EEG, clustering, SVM |
| Fields of Research (2008): | 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified |
| Fields of Research (2020): | 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified |
| Socio-Economic Objectives (2008): | E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering |
| URI: | http://eprints.usq.edu.au/id/eprint/33578 |
Actions (login required)
 |
Archive Repository Staff Only |