Performance Analysis of Soft Decision Trees Models for Fuzzy Based Classification of Epilepsy Risk Levels from EEG Signals
R. Hari Kumar1, T. Vijaya Kumar2
1Dr. R. Harikumar, Professor, ECE Department, Bannari Amman Institute of Technology, Sathyamangalam, India.
2T. Vijayakumar, Assistant Professor, IT Department, Bannari Amman Institute of Technology, Sathyamangalam, India.
Manuscript received on August 10, 2011. | Revised Manuscript received on August 18, 2011. | Manuscript published on September 05, 2011. | PP: 21-27 | Volume-1 Issue-4, September 2011. | Retrieval Number: D075071411/2011©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The purpose of this research is to investigate the feasibility of Game theory based Max-Min optimization of fuzzy outputs for the classification of epilepsy risk levels from EEG (Electroencephalogram) signals. The fuzzy pre classifier is used to classify the risk levels of epilepsy based on extracted parameters like energy, variance, peaks, sharp and spike waves, duration, events and covariance from the EEG signals of the patient. Max-Min SDT (Soft Decision Tree) as post classifier with four methods is applied on the classified data to identify the optimized risk level (singleton) that characterizes the patient’s epilepsy risk level. The efficacy of the above methods is compared based on the bench mark parameters such as Performance Index (PI) and Quality Value (QV). A group of ten patients with known epilepsy findings are used for this study. High PI such as 94.56 % was obtained at QV’s of 22.42 in the SDT optimization when compared to the value of 40% and 6.25 through fuzzy classifier respectively. We identified that the SDT provides a better performing tool for optimizing the epilepsy risk levels
Keywords: EEG Signals, Epilepsy, Fuzzy Logic, Max-Min Soft Decision Trees, Risk Levels