基于压缩感知机理的EEG信号癫痫波形自动检测与识别方法研究

项目名称： 基于压缩感知机理的EEG信号癫痫波形自动检测与识别方法研究

项目编号： No.81201161

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 影像医学与生物医学工程

项目作者： 吴敏

作者单位： 中国人民解放军南京军区南京总医院

项目金额： 23万元

中文摘要： 癫痫病诊断和预防是一个富有挑战性的问题，脑电（EEG）的癫痫特征波形检测和识别是关键。本项目研究EEG信号去伪迹压缩感知的癫痫波形追踪检测和识别。创新性在于：通过EEG信号本源背景波形、非平稳的癫痫特征波以及伪迹等形态分量特征波形的形态核字典构造与形态分量稀疏表示机理研究，建立形态分量稀疏表示的EEG信号去伪迹压缩感知重构模型；研究去伪迹压缩重构EEG的癫痫特征波形追踪模型，提出一套EEG癫痫波形检测的新方法；结合形态分量稀疏表示系数、特征波形表示原子和压缩感知投影测量等三方面的特征信息，提出结合临床诊断和医学知识半监督学习下相关反馈的特征提取和识别两步方案。项目不仅提出一套EEG癫痫波形检测和特征提取的新方法，而且将推动EEG 信号分析在医学、军事、体育等领域的广阔应用前景。

中文关键词： 癫痫；去伪迹；稀疏表示；压缩感知；识别

英文摘要： The diagnosis and prevention of epilepsy is a challenging problem. The essential issue of this problem is the detection and recognition of characteristic waveform in electroencephalogram (EEG) signals. Based on the compressive sensing method ,this project will make an investigation on epilepsy characteristic waveform detection and recognition with the removal of artifacts. The creative contribution will be made as follows: 1) We will first construct a multi-morphological component dictionary for background wave, epilepsy intrinsic wave and artifact's wave, then in the morphological component sparse representation framework ,we will establish compressive sensing(CS) and reconstruction model for EEG. with the removal of artifacts. 2) Using the artifact removal CS model, we will propose a series of methods for epilepsy intrinsic wave pursuit and detection. 3) Three class features such as morphological component sparse representation coefficient, epilepsy intrinsic waveform atom and CS measurements will be integrated to be the distinguishing characteristics, and a two- stage feature selection and recognition theory will be proposed under semi-supervised learning and relevance feedback framework using clinical diagnosis and medical knowledge. Not only the new methods of epilepsy intrinsic wave detection and feature

英文关键词： epileptsy；artifact；spares representation；compressed sensing；recognition