Abualsaud, Khalid and Mahmuddin, Massudi and Saleh, Mohammad and Mohamed, Amr (2014) Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks. In: The 11th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA'2014), November 10-13, 2014, InterContinental, West Bay, Doha, Qatar.

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Abstract

Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems.Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes.In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI.The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands.In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers.Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets.The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	ISBN: 978-1-4799-7100-8/14
Uncontrolled Keywords:	EEG; epileptic seizure; feature extraction; classifiers; classification accuracy.
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	School of Computing
Depositing User:	Dr. Massudi Mahmuddin
Date Deposited:	09 Aug 2015 00:51
Last Modified:	27 Apr 2016 04:18
URI:	https://repo.uum.edu.my/id/eprint/15062

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