用于癫痫发作预测的脑电特征深度学习研究

项目名称： 用于癫痫发作预测的脑电特征深度学习研究

项目编号： No.61501283

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 无线电电子学、电信技术

项目作者： 袁琦

作者单位： 山东师范大学

项目金额： 21万元

中文摘要： 对癫痫发作的准确预测可以为顽固性癫痫患者提供有效的治疗方法，也是植入式闭环癫痫刺激器的关键环节。而作为发作预测技术的核心，脑电(EEG)信号的特征提取已成为阻碍预测技术性能提高的瓶颈。针对人工发掘EEG特征的困难，本项目拟基于深度学习理论，结合极端学习机(ELM)与稀疏编码构建EEG特征的深度学习网络模型，建立可扩展的EEG特征全自动学习途径，并通过构造结构型编码字典进一步提升EEG特征的辨识能力；对于有标记EEG样本少的临床现实，本项目拟充分利用无标记EEG样本，借助ELM的优势设计深度网络的半监督学习方法，提高其泛化性能；另外，拟利用自适应收缩膨胀测度学习方法获取EEG特征的测度距离算法，并通过计算测度距离给出癫痫预发作的风险值。本项目的开展将为EEG特征提取提供新思路与新方法，有助于推进发作预测技术的性能提升，也将推动深度学习在EEG分析领域的应用，具有重要的理论意义及临床应用前景。

中文关键词： 脑电信号；发作预测；深度学习；稀疏编码；半监督学习

英文摘要： The accurate prediction of epileptic seizure can provide the intractable epileptic patients with the effective treatment method and is the key link of implantable closed-loop epilepsy stimulators. As the core of seizure prediction technology, the feature extraction of EEG signals has been the bottleneck which blocks performance improvement of prediction technology. For the difficulties of the artificial construction of EEG features, the project will employ the deep learning theory to build the deep learning network model via combining extreme learning machine (ELM) and sparse coding to establish the extensible way to automatically learn EEG features, and the structural coding dictionary is designed to further improve the identification ability of EEG features. For the actual situation of the shortage of the labeled EEG samples, the project will make the best of unlabeled EEG samples, and develop semi-supervised learning method of the deep learning network utilizing the advantage of ELM to improve its generalization performance. Additionally, the project will propose the algorithm of metric distance of EEG features through shrinkage expansion adaptive metric learning, and offer the risk value of impending seizure using the metric distances of the EEG features. The launch of this project can provide novel ideas and methods for EEG feature extraction, and promote the development of seizure prediction and the application of deep learning in EEG analysis field. There are important theory significance and clinical application prospect for this project.

英文关键词： EEG;seizure prediction;deep learning;sparse coding;semi-supervised learning