Abstract—This paper describes the application of a Simple Random SamplingJ48 (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).

Index Terms—Epilepsy, Simple Random Sampling (SRS), feature extraction, J48.

The authors are with University of Southern Queensland, Toowoomba, 4350, Australia (email: Shuaifang.Wang@usq.edu.au, Guohun.Zhu@usq.edu.au, yan.li@usq.edu.au,peng.wen@usq.edu.au, Bo.Song@usq.edu.au).

 

Cite: Shuaifang Wang, Guohun Zhu, Yan Li, Peng Wen, and Bo Song, "Analysis of Epileptic EEG Signals with Simple Random Sampling J48 Algorithm," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 4, no. 2, pp. 78-81, 2014.