Classification of epileptic eeg signals based on finite impulse response filter and artificial neural networks training algorithms

The electroencephalogram is a powerful tool for understanding the electrical activities of the brain. The automatic and accurate classification of extracranial and intracranial electroencephalogram signals are significant for the evaluation of epilepsy. Electroencephalogram signals contain significant characteristic information about epileptic brain waves. However, the electroencephalogram signals are easily disrupted by the artifacts polluting. This study proposed a clinical decision support system to extract significant epilepsy-related spectral features from the electroencephalogram signal. The artifact-free electroencephalogram signals features were obtained from the Kaiser window based on Finite Impulse Filter. The extracted features were modeled by the Artificial Neural Networks Back Propagation training algorithms which are Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The algorithms' classification performances were compared by the accuracy rates. The experiment results show that compared with the Artificial Neural Networks Back Propagation training algorithms, the performance of the Levenberg-Marquardt is better from the point of accuracy rate which achieves a satisfying classification accuracy of 83.01% for extracranial and intracranial electroencephalogram signals.