Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Neural networks for pattern recognition
Neural networks for pattern recognition
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Support feature machine for classification of abnormal brain activity
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Fault Diagnosis of Generator Based on D-S Evidence Theory
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 01
EEG signal classification using PCA, ICA, LDA and support vector machines
Expert Systems with Applications: An International Journal
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
On the Time Series K-Nearest Neighbor Classification of Abnormal Brain Activity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.