Electroencephalography is one of the important medical methods to evaluate and treat neurophysiology to combat disease related to seizure. The automatic seizure detection system aims to provide a balanced mechanism by excavating deep knowledge of the basic signal and kinetic domains. Contingent alert is given to initiate treatment to reduce the risk of injury in patients with epilepsy and to overcome contingencies. Moreover, the multi-channel Electroencephalogram (EEG) data of seizure detection in conventional machine learning algorithms cannot effectively accommodate both global and spatial information. Therefore in this work proposed a Nonlinear Vector Decomposed Neural Network (NVDN) to detect the seizure from EEG signal. The proposed NVDN based seizure detection system consist of three major stages they are as follows i) EEG preprocessing ii) Feature Extraction and iii) NVDN Classification. In this work, the NVDN technique is applied to improve the accuracy of seizure detection after using frequency domain feature to obtain the results from EEG waves. The performance of the proposed system is validate through MATLAB simulation. The results of the simulation show that the proposed NVDN method is able to effectively detect the seizure with a sensitivity of 94.7%. Specificity of 94.1% and accuracy 95.60%. As compared with conventional methods the proposed system achieve high detecting ratio.

脑电图检查是评估和治疗与癫痫发作有关的疾病的神经生理学的重要医学方法之一。自动癫痫发作检测系统旨在通过挖掘基本信号和动力学域的深入知识来提供一种平衡的机制。给予紧急警报以开始治疗，以减少癫痫患者受伤的风险并克服突发事件。此外，常规机器学习算法中的癫痫发作检测的多通道脑电图（EEG）数据无法有效容纳全局和空间信息。因此，在这项工作中提出了一种非线性矢量分解神经网络（NVDN），用于检测脑电信号中的癫痫发作。所提出的基于NVDN的癫痫发作检测系统包括三个主要阶段，分别是：i）脑电预处理ii）特征提取和iii）NVDN分类。在这项工作中，在使用频域特征从EEG波获得结果之后，将NVDN技术应用于提高癫痫发作检测的准确性。通过MATLAB仿真验证了所提出系统的性能。仿真结果表明，所提出的NVDN方法能够以94.7％的灵敏度有效检测癫痫发作。特异性为94.1％，准确度为95.60％。与常规方法相比，所提出的系统实现了高检测率。在利用频域特征从脑电波中获取结果后，采用NVDN技术提高癫痫发作检测的准确性。通过MATLAB仿真验证了所提出系统的性能。仿真结果表明，所提出的NVDN方法能够以94.7％的灵敏度有效检测癫痫发作。特异性为94.1％，准确度为95.60％。与常规方法相比，所提出的系统实现了高检测率。在利用频域特征从脑电波中获取结果后，采用NVDN技术提高癫痫发作检测的准确性。通过MATLAB仿真验证了所提出系统的性能。仿真结果表明，所提出的NVDN方法能够以94.7％的灵敏度有效检测癫痫发作。特异性为94.1％，准确度为95.60％。与常规方法相比，所提出的系统实现了高检测率。