BACKGROUND:A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain and spinal cord. Monitoring brain activity through electroencephalography (EEG) has become an important tool in the diagnosis of brain disorders. The robust automatic classification of EEG signals is an important step towards detecting a brain disorder in its earlier stages before status deterioration. OBJECTIVE:Motivated by the computation capabilities of natural evolution strategies (NES), this paper introduces an effective automatic classification approach denoted as natural evolution optimization-based deep learning (NEODL). The proposed classifier is an ingredient in a signal processing chain that comprises other state-of-the-art techniques in a consistent framework for the purpose of automatic EEG classification. METHODS:The proposed framework consists of four steps. First, the L1-principal component analysis technique is used to enhance the raw EEG signal against any expected artifacts or noise. Second, the purified EEG signal is decomposed into a number of sub-bands by applying the wavelet transform technique where a number of spectral and statistical features are extracted. Third, the extracted features are examined using the artificial bee colony approach in order to optimally select the best features. Lastly, the selected features are treated using the proposed NEODL classifier, where the input signal is classified according to the problem at hand. RESULTS:The proposed approach is evaluated using two benchmark datasets and addresses two neurological disorder applications: epilepsy disease and motor imagery. Several experiments are conducted where the proposed classifier outperforms other deep learning techniques as well as other existing approaches. CONCLUSION:The proposed framework, including the proposed classifier (NEODL), has a promising performance in the classification of EEG signals, including epilepsy disease and motor imagery. Based on the given results, it is expected that this approach will also be useful for the identification of the epileptogenic areas in the human brain. Accordingly, it may find application in the neuro-intensive care units, epilepsy monitoring units, and practical brain-computer interface systems in clinics.

中文翻译：

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基于自然进化优化的神经疾病分类的深度学习算法。

背景：神经系统疾病是神经系统的重要问题之一，它影响人脑和脊髓的基本功能。通过脑电图（EEG）监视大脑活动已成为诊断脑部疾病的重要工具。脑电信号的强大自动分类是在状态恶化之前检测其早期阶段脑部疾病的重要步骤。目的：受自然进化策略（NES）的计算能力的推动，本文介绍了一种有效的自动分类方法，称为基于自然进化优化的深度学习（NEODL）。所提出的分类器是信号处理链中的一个组成部分，它在一致的框架中包括其他最新技术，以实现自动EEG分类。方法：拟议的框架包括四个步骤。首先，使用L1主要成分分析技术来增强原始EEG信号以抵抗任何预期的伪影或噪声。其次，通过应用小波变换技术将纯化的EEG信号分解为多个子带，其中提取了许多频谱和统计特征。第三，使用人工蜂群方法检查提取的特征，以便最佳地选择最佳特征。最后，使用建议的NEODL分类器对所选特征进行处理，其中根据当前问题对输入信号进行分类。结果：使用两个基准数据集评估了所提出的方法，并解决了两种神经系统疾病的应用：癫痫病和运动图像。在建议的分类器优于其他深度学习技术和其他现有方法的地方进行了一些实验。结论：所提出的框架，包括所提出的分类器（NEODL），在包括癫痫病和运动图像在内的EEG信号分类中具有有希望的表现。根据给定的结果，预计该方法也将用于识别人脑中的致痫区域。因此，它可以在临床中的神经重症监护病房，癫痫监测病房和实用的脑机接口系统中找到应用。