Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Detection of seizure activity in EEG by an artificial neural network: a preliminary study
Computers and Biomedical Research
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Wavelet analysis of generalized tonic-clonic epileptic seizures
Signal Processing
Computers in Biology and Medicine
Feature selection and blind source separation in an EEG-based brain-computer interface
EURASIP Journal on Applied Signal Processing
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
Non-linear analysis of EEG signals at various sleep stages
Computer Methods and Programs in Biomedicine
Computers and Industrial Engineering
Expert model for detection of epileptic activity in EEG signature
Expert Systems with Applications: An International Journal
Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm
Computers in Biology and Medicine
Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection
EURASIP Journal on Advances in Signal Processing
Expert Systems with Applications: An International Journal
Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study
Artificial Intelligence in Medicine
Discrete harmony search based expert model for epileptic seizure detection in electroencephalography
Expert Systems with Applications: An International Journal
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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In this study, a new scheme was presented for the optimal classification of epileptic seizures in EEG using wavelet analysis and the genetic algorithm (GA). In the proposed scheme, normal and epileptic EEG epochs (windows) were decomposed into various frequency bands through a fourth-level wavelet packet decomposition. Approximate entropy (ApEn) values of the wavelet coefficients at all nodes of the decomposition tree were used as a feature set to characterize the predictability of the EEG data within the corresponding frequency bands. Then, the GA was used to find the optimal feature subset that maximizes the classification performance of a learning vector quantization (LVQ)-based normal and epileptic EEG classifier. Clinical EEG data recorded from normal subjects and epileptic patients were used to test the performance of the new scheme. It was demonstrated that the new scheme was able to classify the normal and epileptic EEG epochs with 94.3% and 98% accuracy, respectively. It was also shown that, if the GA was not used for the optimal feature selection, the classification accuracies dropped noticeably.