Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
GPSM: a Generaized Probabilistic Semantic Model for ambiguity resolution
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
EURASIP Journal on Applied Signal Processing
Wavelet transform for processing power quality disturbances
EURASIP Journal on Applied Signal Processing
A new approach for epileptic seizure detection using adaptive neural network
Expert Systems with Applications: An International Journal
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
Expert model for detection of epileptic activity in EEG signature
Expert Systems with Applications: An International Journal
Epileptic seizure detection using dynamic wavelet network
Expert Systems with Applications: An International Journal
Survey A survey on applications of the harmony search algorithm
Engineering Applications of Artificial Intelligence
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Seizure detection and classification using signal processing methods has been an important issue of research for the last two decades. In the present study, a novel scheme was presented to detect epileptic seizure activity with very fast and highest accuracy from background electro encephalogram (EEG) data recorded from epileptic and normal subjects. The proposed scheme is based on discrete wavelet packet transform (DWT) with energy, entropy, standard deviation, mean, kurtosis, skewness and entropy estimation at each node of the decomposition tree followed by application of probabilistic neural network (PNN). Normal as well as epileptic EEG epochs were decomposed into approximation and details coefficients till sixth-level using DWT packet. Discrete harmony search with modified differential operator was used to select the optimal features out of all above mentioned statistical and non-statistical parameters. In order to demonstrate the efficacy of the proposed algorithm for classification purpose using PNN, we have implemented 10-fold cross validation. Clinical EEG data recorded from normal as well as epileptic subjects are used to test the performance of this new scheme. It is found that the detection rate is 100% accurate with same level of sensitivity and specificity.