Classification of EEG Signal by Training Neural Network with Swarm Optimization for Identification of Epilepsy

Iqra Tahir, Usman Qamar, Hassan Abbas, Babar Zeb, Sana Abid
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引用次数: 3

Abstract

EEG signal classification is a pivotal task for identification of different brain related disorders. The paper is about classification of EEG signal presenting a novel approach for the identification of whether the seizure is epileptic or normal that technique is based on training of neural network with having improved simplified swarm optimization algorithm. Our proposed methodology is evaluated with different parameters and testing accuracy of 94 % is reported for a publicly available dataset.
基于群优化训练神经网络的脑电信号分类癫痫识别
脑电信号分类是识别不同脑相关疾病的关键任务。本文对脑电图信号进行分类,提出了一种基于神经网络训练的识别癫痫发作与正常发作的新方法,该方法采用改进的简化群优化算法。我们提出的方法用不同的参数进行了评估,并且在公开可用的数据集上报告了94%的测试精度。
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