用于癫痫发作预测的卷积神经网络
Convolutional Neural Networks for Epileptic Seizure Prediction
AI总结：
本文提出用卷积神经网络直接处理颅内脑电图进行癫痫发作预测，避免手工特征提取，并在狗和患者的公开数据集上验证了其适用性。
摘要：
癫痫是最常见的神经系统疾病，对发作的准确预测将有助于克服患者的不确定性和无助感。在这项工作中，我们提出并讨论了一种用于癫痫发作预测的颅内脑电图（iEEG）分类的新方法。与以往的方法不同，我们明确避免提取手工设计的特征，而是使用卷积神经网络（CNN）拓扑结构来同时确定合适的信号特征，并对发作前期和发作间期片段进行二分类。我们在包含四只狗和三名患者长期记录的公开数据集上评估了三种不同的模型。总体而言，我们的研究结果证明了该方法的普遍适用性。在这项工作中，我们讨论了所提方法的优势和局限性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
