In epilepsy, patients suffer from seizures which cannot be controlled with medicines or surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is extremely important so that they can be controlled with medication before they actually occur. Researchers have proposed multiple machine/deep learning based methods to predict epileptic seizures; however, accurate prediction of epileptic seizures with low false positive rate is still a challenge. In this research, we propose a deep learning based ensemble learning method to predict epileptic seizures. In the proposed method, EEG signals are preprocessed using empirical mode decomposition followed by bandpass filtering for noise removal. The class imbalance problem has been mitigated with synthetic preictal segments generated using generative adversarial networks. A three-layer customized convolutional neural network has been proposed to extract automated features from preprocessed EEG signals and combined them with handcrafted features to get a comprehensive feature set. The feature set is then used to train an ensemble classifier that combines the output of SVM, CNN and LSTM using Model agnostic meta learning. An average sensitivity of 96.28% and specificity of 95.65% with an average anticipation time of 33 min on all subjects of CHBMIT has been achieved by the proposed method, whereas, on American epilepsy society-Kaggle seizure prediction dataset, an average sensitivity of 94.2% and specificity of 95.8% has been achieved on all subjects.

在癫痫中，超过 30% 的患者会出现无法通过药物或手术治疗控制的癫痫发作。癫痫发作的预测非常重要，以便在它们实际发生之前用药物控制它们。研究人员提出了多种基于机器/深度学习的方法来预测癫痫发作；然而，以低假阳性率准确预测癫痫发作仍然是一个挑战。在这项研究中，我们提出了一种基于深度学习的集成学习方法来预测癫痫发作。在所提出的方法中，使用经验模式分解对 EEG 信号进行预处理，然后进行带通滤波以去除噪声。类不平衡问题已经通过使用生成对抗网络生成的合成前段得到缓解。提出了一种三层定制卷积神经网络，用于从预处理的 EEG 信号中提取自动化特征，并将它们与手工特征相结合，以获得全面的特征集。然后使用该特征集训练一个集成分类器，该分类器使用模型不可知元学习将 SVM、CNN 和 LSTM 的输出相结合。该方法对 CHBMIT 的所有受试者的平均灵敏度为 96.28%，特异性为 95.65%，平均预期时间为 33 分钟，而在美国癫痫学会-Kaggle 癫痫预测数据集上，平均灵敏度为 94.2%并且在所有科目上都达到了 95.8% 的特异性。