ECoG,EEG-fMRI多模态癫痫监测与病灶定位研究

项目名称： ECoG,EEG-fMRI多模态癫痫监测与病灶定位研究

项目编号： No.81471743

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 医药、卫生

项目作者： 唐晓英

作者单位： 北京理工大学

项目金额： 73万元

中文摘要： 癫痫是一种发病率高的大脑神经系统疾病，具有突发性和反复性，对患者的生命安全构成极大的威胁。有效监测癫痫和对病灶定位对于该病的预防和治疗具有重要意义。单一因素无创监测癫痫发作存在精确度太低的问题，而目前主流的皮层脑电癫痫灶定位方法，存在无法检测全脑脑电信号并且有创的问题。本研究拟利用现有的皮层脑电信号检测方法与结果为基础，采用分数阶傅里叶变换提取脑电特效特征，用支持向量机算法建立分类标准，实现癫痫发作自动监测；同时，分析研究头皮脑电（sEEG）、皮层脑电（cEEG）与fMRI多模态信息的相关性，建立多模态癫痫信息传递模型；模拟内层脑电信号向外传播过程，获得sEEG与cEEG信号间的位置、强度对应关系，研究利用基于高频振荡信号（HFOs）的希尔伯特变换方法对皮层脑电信号分析结果，确定头皮脑电信号结合fMRI致病灶定位新方法，探索出一种多模态无创监测癫痫发作和病灶定位的新思路和新方法。

中文关键词： 功能磁共振；脑电图；信号处理；脑网络；生物医学工程

英文摘要： Epilepsy has been the hot point for cerebral disease in recent years. It is a kind of cerebral neuron system disease, which have characters of high morbidity、burstiness and repeatability. All of these characters together puts tremendous threats on patients' life security. Effective detection and localization of epilepsy have important clinical significance. Single module for noninvasive epilepsy prediction cannot offer a good prediction on epilepsy due to its low accuracy. Cortex EEG(ECoG) analysis method belongs to main stream methods for epilepsy diagnosis, however, this method has limitations like invasive detection and cannot get signal from whole part of cerebrum, all these side effects bring agony to epilepsy patients. Our research will combine EEG and MR technique, offers a new method for noninvasive epilepsy detection and localization. Our research based on previous EEG research methods and results, use fractional Fourier transform method to select signal features and then use support vector machine method to set standards for classification of epilepsy signal. We use the classification result to realize automatic epilepsy detection; At the meantime, we analysis ECoG signal、scalp EEG(sEEG) signal and Functional Magnetic Resonance data to reveal the relationship between these signals, we will also set up a brain signal transportation model to symbolize the relation. This model will be used to simulate the process of inner EEG signal transport to outside, thus we can obtain the relationship of intensity and position aspects between inner EEG signal and outside EEG signal. We will combine the localization result predicted by High Frequency Oscillations, and the EEG-fMRI method to get the final localization result. Finally, we will build a model to detect and localize signal which will lay the foundation for further noninvasive epilepsy detection and localization.

英文关键词： fMRI;EEG;signal process;brain network;biomedical engineering