Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Handwritten Digits Recognition Using Particle Swarm Optimization
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
Epileptic EEG detection using neural networks and post-classification
Computer Methods and Programs in Biomedicine
Theoretical advances in artificial immune systems
Theoretical Computer Science
Hardware Acceleration of an Immune Network Inspired Evolutionary Algorithm for Medical Diagnosis
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
Cross-correlation aided support vector machine classifier for classification of EEG signals
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Expert Systems with Applications: An International Journal
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
Entropies based detection of epileptic seizures with artificial neural network classifiers
Expert Systems with Applications: An International Journal
Epileptic seizure detection using dynamic wavelet network
Expert Systems with Applications: An International Journal
Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines
Expert Systems with Applications: An International Journal
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The diagnosis of epilepsy from EEG signals by a human scorer is a very time consuming and costly task considering the large number of epileptic patients admitted to the hospitals and the large amount of data needs to be scored. In this paper, a hybrid method called adaptive particle swarm negative selection (APSNS) was introduced to automate the process of epileptic seizures detection in EEG signals. In the proposed method, an adaptive negative selection creates a set of artificial lymphocytes (ALCs) that are tolerant to normal patterns. However, the particle swarm optimization (PSO) algorithm forces these ALCs to explore the space of epileptic signals and maintain diversity and generality among them. The EEG signals were analyzed using discrete wavelet transform (DWT) to extract the most important information needed for decision making. The features extracted have been used to investigate the performance of the proposed APSNS algorithm in classifying the EEG signals. The Experimental results confirm effectiveness and stability of the proposed method. Its classification accuracy outperforms many of the methods in the literature.