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首页> 外文期刊>Uludag University Journal of The Faculty of Engineering >CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS
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CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS

机译：基于有限脉冲响应滤波器和人工神经网络训练算法的癫痫脑电图信号分类

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摘要

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.
机译：脑电图是理解脑电图的电气活动的强大工具。颅外和颅内脑电图的电气活动的分类对于癫痫的评估是显着的.elelentcalogram信号包含关于癫痫脑波的显着特征信息。但是，脑电图信号伪影污染容易破坏。本研究提出了一种临床决策支持系统，以从脑电图信号中提取显着的癫痫相关光谱特征。根据有限脉冲滤波器从Kaiser窗口获得伪影型脑电图信号特征。提取的由人工神经网络反馈传播训练算法建模的特征，该算法是Levenberg-Marquardt，贝叶斯正则化和缩放的共轭梯度。通过精度率比较了算法的分类性能。实验结果表明，与人工神经网络的反向传播训练算法相比，Levenberg-Marquardt的性能从精度率较好，这使得颅内和颅内脑电图信号的满足分类精度为83.01％。

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