Numerous techniques have been explored so far for epileptic electroencephalograph (EEG) signal detection and classification. Deep learning-based approaches are in recent demand for data classification with huge features. In this paper, an improved deep learning approach based on convolutional features followed by stacked autoencoder (CSAE) and kernel extreme learning machine (KELM) classifier at the end is proposed for EEG signal classification. The convolutional network extracts initial features by convolution, and after this stage, the features are supplied to stacked autoencoder (SAE) for obtaining final compressed features. These suitable features are then fed to KELM classifier for identifying seizure, seizure-free and healthy EEG signals. The EEG signals are decomposed through chaotic water cycle algorithm-optimised variational mode decomposition (CWCA-OVMD) from which the optimised number of efficient modes is obtained yielding six features like energy, entropy, standard deviation, variance, kurtosis, and skewness. These CWCA-OVMD-based features are then fed to the CSAE for the extraction of relevant features. Once the features are obtained, the KELM classifier is used to classify the EEG signal. The classification results are compared with different deep learning classifiers validating the efficacy of the proposed model. The KELM classifier avoids the choice of hidden neurons in the end layer unlike traditional classifiers which is one of the major advantages.

迄今为止，人们已经探索了许多用于癫痫脑电图（EEG）信号检测和分类的技术。最近需要基于深度学习的方法来进行具有巨大特征的数据分类。本文提出了一种基于卷积特征、最后是堆叠自动编码器（CSAE）和内核极限学习机（KELM）分类器的改进深度学习方法，用于脑电信号分类。卷积网络通过卷积提取初始特征，在此阶段之后，将特征提供给堆叠自动编码器（SAE）以获得最终的压缩特征。然后将这些合适的特征输入 KELM 分类器，以识别癫痫发作、无癫痫发作和健康的脑电图信号。通过混沌水循环算法优化的变分模式分解（CWCA-OVMD）对脑电信号进行分解，从中获得有效模式的优化数量，从而产生能量、熵、标准差、方差、峰度和偏度等六个特征。然后，这些基于 CWCA-OVMD 的特征被馈送到 CSAE 以提取相关特征。一旦获得特征，KELM分类器就用于对EEG信号进行分类。将分类结果与不同的深度学习分类器进行比较，验证了所提出模型的有效性。与传统分类器不同，KELM 分类器避免了在末端层选择隐藏神经元，这是主要优点之一。