期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2011
卷号：1
期号：4
页码：206-211
出版社：International Journal of Soft Computing & Engineering
摘要：The objective of this paper is to design, simulate, and synthesis a simple, suitable and reliable Soft Decision Trees for classification of epilepsy risk levels from EEG signals. The fuzzy classifier (level one) 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. Soft Decision Tree (post classifier with max-min and min-max criteria) of three models is applied on the classified data to identify the optimized risk level (singleton) which characterizes the patient’s risk level. The efficacy of these methods is compared with the bench mark parameters such as Performance Index (PI), and Quality Value (QV). A group of twenty patients with known epilepsy findings are analyzed. High PI such as 95.88 % was obtained at QV’s of 22.43 in the SDT model of (16-4-2-1) with Method-II (min-max criteria) when compared to the value of 40% and 6.25 through fuzzy classifier respectively. It was observed that the simulated and synthesized Field Programmable Gated Array (FPGA) SDT models are good post classifier in the optimization of epilepsy risk levels which is closely follows the mat lab version. the deterministic character of dynamics of the underlying system.
