Epileptic Seizure Detection Using Lacunarity and Bayesian Linear Discriminant Analysis in Intracranial EEG

2013 ◽  
Vol 60 (12) ◽  
pp. 3375-3381 ◽  
Author(s):  
Weidong Zhou ◽  
Yinxia Liu ◽  
Qi Yuan ◽  
Xueli Li
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
Intracranial Eeg ◽  
Epileptic Seizure Detection ◽  
Linear Discriminant ◽  
Bayesian Linear Discriminant Analysis

Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals

2012 ◽  
Vol 3 (2) ◽  
pp. 27-44
Author(s):  
Abduljalil Mohamed ◽  
Khaled Bashir Shaban ◽  
Amr Mohamed
Keyword(s):  
Epileptic Seizure ◽  
Classification Accuracy ◽  
Evidence Theory ◽  
Seizure Detection ◽  
Support Vector ◽  
Brain State ◽  
Eeg Signals ◽  
Epileptic Seizure Detection ◽  
Linear Discriminant ◽  
Detection Techniques

Different brain states and conditions can be captured by electroencephalogram (EEG) signals. EEG-based epileptic seizure detection techniques often reduce these signals into sets of discriminant features. In this work, an evidence theory-based approach for epileptic detection, using several classifiers, is proposed. Within the framework of the evidence theory, each of these classifiers is considered a source of information and given a certain weight based on both its overall classification accuracy as well as its precision rate for the respective brain state. These sources are fused using the Dempster’s rule of combination. Experimental work is done where five time domain features are obtained from EEG signals and used by a set classifiers, namely, Bayesian, K-nearest neighbor, neural network, linear discriminant analysis, and support vector machine classifiers. Higher classification accuracy of 89.5% is achieved, compared to 75.07% and 87.71% accuracy obtained from the worst and best used classifiers.

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