Traditionally, detection of epileptic seizures based on the visual inspection of neurologists is tedious, laborious and subjective. To overcome such disadvantages, numerous seizure detection techniques involving signal processing and machine learning tools have been developed. However, there still remain the problems of automatic detection with high efficiency and accuracy in distinguishing normal, interictal and ictal electroencephalogram (EEG) signals. In this study we propose a novel method for automatic identification of epileptic seizures in singe-channel EEG signals based upon time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks. First, EEG signals are decomposed into a series of proper rotation components (PRCs) and a baseline signal by using the ITD method. The first two PRCs of the EEG signals are extracted, which contain most of the EEG signals’ energy and are considered to be the predominant PRCs. Second, four levels DWT is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies (db3) wavelet function is selected for analysis. Third, phase space of the PRCs is reconstructed based on db3, in which the properties associated with the nonlinear EEG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in EEG system dynamics between normal, interictal and ictal EEG signals. Fourth, neural networks are then used to model, identify and classify EEG system dynamics between normal (healthy), interictal and ictal EEG signals. Finally, experiments are carried out on the University of Bonn’s widely used and publicly available epilepsy dataset to assess the effectiveness of the proposed method. By using the 10-fold cross-validation style, the achieved average classification accuracy for eleven cases is reported to be 98.15%. Compared with many state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic detection of seizure EEG signals in the clinical application.

中文翻译：

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使用时间尺度分解 (ITD)、离散小波变换 (DWT)、相空间重建 (PSR) 和神经网络识别 EEG 信号中的癫痫发作

传统上，基于神经科医生的视觉检查来检测癫痫发作是繁琐、费力和主观的。为了克服这些缺点，已经开发了许多涉及信号处理和机器学习工具的癫痫检测技术。然而，在区分正常、发作间期和发作期脑电图(EEG)信号方面，仍然存在高效、准确的自动检测问题。在这项研究中，我们提出了一种基于时间尺度分解 (ITD)、离散小波变换 (DWT)、相空间重建 (PSR) 和神经网络的单通道 EEG 信号自动识别癫痫发作的新方法。首先，使用 ITD 方法将 EEG 信号分解为一系列适当的旋转分量 (PRC) 和基线信号。提取脑电信号的前两个PRC，其中包含大部分EEG信号的能量，被认为是占优势的PRC。其次，采用四级DWT将主要PRC分解为不同的频段，其中选择三阶Daubechies（db3）小波函数进行分析。第三，PRC 的相空间是基于 db3 重建的，其中与非线性 EEG 系统动力学相关的属性被保留。三维 (3D) PSR 与欧几里得距离 (ED) 一起被用于推导特征，这表明正常、发作间和发作期 EEG 信号之间的 EEG 系统动力学存在显着差异。第四，然后使用神经网络对正常（健康）、发作间期和发作期 EEG 信号之间的 EEG 系统动力学进行建模、识别和分类。最后，实验是在波恩大学广泛使用和公开可用的癫痫数据集上进行的，以评估所提出方法的有效性。通过使用 10 折交叉验证风格，据报道，11 个案例的平均分类准确率为 98.15%。与许多最先进的方法相比，结果证明了优越的性能，并且所提出的方法可以作为临床应用中癫痫脑电信号自动检测的潜在候选者。