标题：Performance Analysis of Patient Specific Epilepsy Risk Level Classifications from EEG Signals Using Two Tier Hybrid (Fuzzy, Soft Decision Trees Models and MLP Neural Networks) Classifiers
期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2012
卷号：2
期号：2
页码：541-549
出版社：International Journal of Soft Computing & Engineering
摘要：This paper compares the performance analysis of a two tier hybrid Fuzzy, Soft Decision Tree (SDT) models and Multi layer Perceptron (MLP) neural networks in optimization of patient specific epilepsy risk levels classifications from EEG (Electroencephalogram) 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 and MLP neural networks are applied on the classified data to identify the optimized risk level (singleton) which characterizes the patient’s state. The efficacies of these methods are compared with the bench mark parameters such as Performance Index (PI), Sensitivity, Specificity 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) and for MLP (4-4-1) 99.9%and 24.43 when compared to the value of 40% and 6.25 through fuzzy classifier respectively. It was identified that the SDT models and MLP (4-4-1) are good post classifier in the optimization of epilepsy risk levels. SDT models were well accounted for low training cost over heads. A part from the training cost MLP neural networks outperformed SDT classifiers in classifying the epilepsy risk levels.
