Analysis of epileptic EEG signals with simple random sampling J48 algorithm

Wang, Shuaifang and Zhu, Guohun and Li, Yan and Wen, Peng and Song, Bo (2014) Analysis of epileptic EEG signals with simple random sampling J48 algorithm. International Journal of Bioscience, Biochemistry and Bioinformatics, 4 (2). pp. 78-81. ISSN 2010-3638

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Abstract

This paper describes the application of a Simple Random Sampling J48 (SRS-J48) model for classification of electroencephalogram (EEG) signals. Decision making is performed in two stages: feature extraction and classification. Eight statistical features are extracted from a two-level sample set model based on SRS technique and then classified by the J48 decision tree algorithm in Weka. The classification accuracy of the SRS-J48 is 16.35% higher than that of J48 according to the five groups of experiment with only 13% execution time on average. Besides, the proposed SRS-J48 algorithm has competitive or even better results on some of the experimental groups than Siuly’s Simple Random Sampling-Least Square-Support Vector Machine (SRS-LS-SVM).


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: No indication of copyright restrictions. 'International Journal of Bioscience, Biochemistry and Bioinformatics (IJBBB) is an international academic open access journal'
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 20 Jan 2015 07:05
Last Modified: 18 Dec 2017 06:37
Uncontrolled Keywords: epilepsy; simple random sampling (SRS); feature extraction
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Socio-Economic Objective: C Society > 92 Health > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920111 Nervous System and Disorders
Identification Number or DOI: 10.7763/IJBBB.2014.V4.314
URI: http://eprints.usq.edu.au/id/eprint/24965

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