Analysis of epileptic EEG signals with simple random sampling J48 algorithm

Wang, Shuaifang and Zhu, Guohun and Li, Yan ORCID: https://orcid.org/0000-0002-4694-4926 and Wen, Peng ORCID: https://orcid.org/0000-0003-0939-9145 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/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: 20 Jan 2015 07:05
Last Modified: 18 Dec 2017 06:37
Uncontrolled Keywords: epilepsy; simple random sampling (SRS); feature extraction
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing
Socio-Economic Objectives (2008): C Society > 92 Health > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920111 Nervous System and Disorders
Identification Number or DOI: https://doi.org/10.7763/IJBBB.2014.V4.314
URI: http://eprints.usq.edu.au/id/eprint/24965

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