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首页> 外文期刊>Journal of ambient intelligence and humanized computing >Brain epilepsy seizure detection using bio-inspired krill herd and artificial alga optimized neural network approaches
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Brain epilepsy seizure detection using bio-inspired krill herd and artificial alga optimized neural network approaches

机译：脑癫痫癫痫发作检测使用生物启发磷虾群和人工藻类优化神经网络方法

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摘要

Nowadays, Epilepsy is one of the chronic severe neurological diseases; it has been identified with the help of brain signal analysis. The brain signals are recorded with the help of electrocorticography (ECoG), Electroencephalogram (EEG). From the brain signal, the abnormal brain functions are a more challenging task. The traditional systems are consuming more time to predict unusual brain patterns. Therefore, in this paper, effective bio-inspired machine learning techniques are utilized to predict the epilepsy seizure from the EEG signal with maximum recognition accuracy. Initially, patient brain images are collected by placing the electrodes on their scalp. From the brain signal, different features are extracted that are analyzed with the help of the Krill Herd algorithm for selecting the best features. The selected features are processed using an artificial alga optimized general Adversarial Networks. The network recognizes the intricate and abnormal seizure patterns. Then the discussed state-of-art methods are examined simulation results.
机译：如今，癫痫是慢性严重的神经疾病之一;已经借助脑信号分析鉴定。借助电压术（ECOG），脑电图（EEG）记录大脑信号。从大脑信号，异常大脑功能是一个更具挑战性的任务。传统系统正在消耗更多时间来预测不寻常的脑模式。因此，在本文中，利用有效的生物启发机学习技术以最大识别精度从脑电图信号预测癫痫癫痫发作。最初，通过将电极放置在其头皮上来收集患者脑图像。从大脑信号，提取不同的特征，以便在KRILL群算法的帮助下进行分析，以选择最佳特征。使用人工藻类优化的普通对抗网络处理所选特征。该网络识别错综复杂的癫痫发作模式。然后，讨论了所讨论的最先进的方法是检查仿真结果。

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