Epileptic seizure detection from the brain EEG signals is an essential step for speeding up the diagnosis that assists researchers and medical professionals. For this, various classification signal processing techniques have been developed in the traditional works. Still, they limit with the problems of increased complexity, reduced performance and insufficient classification rate. This motivates to design an automatic system for classifying the normal and abnormal EEG signals. Thus, an efficient machine learning approaches are implemented in this work, to overcome the existing techniques limitations. Here, an enhanced curvelet transform technique is established in order to overcome the disadvantage of Gabor and Wavelet transformations data loss and indiscriminate orientations. This method has the capacity to furnish the all the signals data required with no information loss of shearlet transformation and hence implemented to preprocess the given EEG signal, which smoothen the signal by eliminating the noise. Then, a modified graph theory, fractal dimension and novel pattern transformation techniques are employed to extract the features and patterns. The extraction of features is crucial for classification and compression of huge volume of EEG signal that possess low information. This theory improves the precision and speed of the computational technique. Most of the current research, Graph theory is reflected in the area of quantitative description of the time series data. It is predominantly employed for the reduction of noise and not affected during choosing the factors. From the patterns, the statistical features are extracted by using a standard gray level co-occurrence matrix technique that comprises entropy, homogeneity, energy, correlation and maximum probability. This method calculates the linear dependency of the adjacent samples thereby effective measurement of information loss in the transmitted signal is accomplished. Then, these extracted features are fed to the classifier named as novel random forest classification for detecting and classifying the signal as healthy, ictal and interictal. During simulation, various performance measures have been used for evaluating the results of existing and proposed classification techniques and results validate the efficacy of proposed technique.

中文翻译：

---

用于癫痫发作检测的混合特征提取和机器学习方法

从大脑 EEG 信号中检测癫痫发作是加速诊断的重要步骤，有助于研究人员和医疗专业人员。为此，在传统工作中已经开发了各种分类信号处理技术。尽管如此，它们仍然存在复杂性增加、性能降低和分类率不足的问题。这促使设计一个自动系统来对正常和异常 EEG 信号进行分类。因此，在这项工作中实施了一种有效的机器学习方法，以克服现有技术的局限性。在这里，为了克服 Gabor 和小波变换数据丢失和方向不分明的缺点，建立了一种增强型曲波变换技术。该方法能够提供所需的所有信号数据而不会丢失剪切波变换的信息，因此可以对给定的脑电信号进行预处理，通过消除噪声来平滑信号。然后，采用改进的图论、分形维数和新颖的模式转换技术来提取特征和模式。特征的提取对于海量低信息量的脑电信号的分类和压缩至关重要。该理论提高了计算技术的精度和速度。目前的大部分研究，图论都体现在时间序列数据的定量描述领域。它主要用于降低噪声，在选择因素时不受影响。从花纹来看，统计特征是通过使用包括熵、同质性、能量、相关性和最大概率的标准灰度共生矩阵技术提取的。该方法计算相邻样本的线性相关性，从而实现对传输信号中信息损失的有效测量。然后，这些提取的特征被馈送到称为新随机森林分类的​​分类器，用于检测和分类信号为健康、发作期和间歇期。在模拟过程中，各种性能指标已被用于评估现有和提议的分类技术的结果，结果验证了提议技术的有效性。相关性和最大概率。该方法计算相邻样本的线性相关性，从而实现对传输信号中信息损失的有效测量。然后，这些提取的特征被馈送到称为新随机森林分类的​​分类器，用于检测和分类信号为健康、发作期和间歇期。在模拟过程中，各种性能指标已被用于评估现有和提议的分类技术的结果，结果验证了提议技术的有效性。相关性和最大概率。该方法计算相邻样本的线性相关性，从而实现对传输信号中信息损失的有效测量。然后，这些提取的特征被馈送到称为新随机森林分类的​​分类器，用于检测和分类信号为健康、发作期和间歇期。在模拟过程中，各种性能指标已被用于评估现有和提议的分类技术的结果，结果验证了提议技术的有效性。这些提取的特征被馈送到称为新随机森林分类的​​分类器，用于检测和分类信号为健康、发作期和间歇期。在模拟过程中，各种性能指标已被用于评估现有和提议的分类技术的结果，结果验证了提议技术的有效性。这些提取的特征被馈送到称为新随机森林分类的​​分类器，用于检测和分类信号为健康、发作期和间歇期。在模拟过程中，各种性能指标已被用于评估现有和提议的分类技术的结果，结果验证了提议技术的有效性。