Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient's uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary to previous approaches, we categorically refrain from an extraction of hand-crafted features and use a convolutional neural network (CNN) topology instead for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segments. Three different models have been evaluated on public datasets with long-term recordings from four dogs and three patients. Overall, our findings demonstrate the general applicability. In this work we discuss the strengths and limitations of our methodology.

翻译：癫痫是最常见的神经系统疾病，准确预测发作有助于减轻患者的不确定性和无助感。本文提出并讨论了一种基于颅内脑电图（iEEG）分类的癫痫发作预测新方法。与以往方法不同，我们明确摒弃手工特征提取，转而采用卷积神经网络（CNN）拓扑结构，同时实现适宜信号特征的确定以及发作前期与发作间期片段的二分类任务。我们基于公开数据集（包含四只犬和三名患者的长期记录）评估了三种不同模型。总体而言，我们的研究结果验证了该方法的普适性。本文进一步讨论了该方法的优势与局限性。