Performance Analysis of Epileptic Seizure Detection System Using Neural Network Approach

R. Vaitheeshwari, V. SathieshKumar
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引用次数: 12

Abstract

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).
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基于神经网络的癫痫发作检测系统性能分析
近年来,许多人受到一种叫做癫痫或癫痫发作的常见神经系统疾病的影响。它在没有任何症状的情况下突然发生,从而增加了人类的死亡率。为了在癫痫发作前警告患者,需要一个可靠的预警系统。因此,提出的研究工作旨在建立一个人工神经网络模型,在癫痫发作之前检测和预测癫痫事件。所提出的人工神经网络模型结构简单、高效,预测和检测癫痫事件的灵敏度为91.15%。数据的实验测试表明,该方法的预测精度为91%,计算时间为630秒。
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