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Performance Analysis of Epileptic Seizure Detection System Using Neural Network Approach

机译:利用神经网络方法的癫痫癫痫发作检测系统性能分析

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In recent years, numerous people are affected by a common neurological disorder called Epilepsy or Epileptic seizure. It occurs abruptly without any symptoms and thus increases the mortality rate of the humans. In order to warn the patient prior to the onset of seizure, a reliable warning system is needed. Thus the proposed research work aim to create an artificial neural network model to detect and predict the seizure event before its onset. The proposed Artificial Neural Network model is simple and efficient architecture that predict and detect the seizure event at the sensitivity rate of 91.15%. Experimental testing of the data show that prediction accuracy is 91% with considerable amount of computation time (630 seconds).
机译:近年来,许多人受到含有癫痫或癫痫发作的常见神经障碍的影响。它突然出现而没有任何症状,从而增加了人类的死亡率。为了在癫痫发作之前警告患者,需要一种可靠的警告系统。因此,所提出的研究工作旨在创建一个人工神经网络模型来检测和预测其发作前的癫痫发作事件。所提出的人工神经网络模型是简单且有效的架构,其以91.15%的灵敏度预测和检测癫痫发作事件。数据的实验测试显示,预测精度是91%,具有相当大的计算时间(630秒)。

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