摘要
目的对一维的多通道非平稳信号EEG随机非自适应地欠采样,高质量地重建原信号,从而实现EEG信号的自动检测与分析。方法实验中以高斯函数及其一、二阶导数为原子的生成函数,构建了一个新的冗余多成份字典.随机高斯测量阵为测量矩阵,按压缩感知测量模型重建信号,并采用了稀疏逼近误差NMSE作为逼近程度的定量度量标准,判定实验结果。结果所选字典中原子可更加有效地匹配EEG信号中的多种瞬时特征波形,从而能够对EEG信号形成更为稀疏的匹配追踪分解。基于压缩感知理论的信号采样只需使用不到原信号一半的样本数,即可高质量地重建原信号,对于重要的瞬时特征波形能够很好地保持。结论基于压缩感知理论的信号采样包含了原信号的足够信息,利用EEG信号的稀疏性(或可压缩性)先验条件,通过一定的线性或非线性的解码模型可以以很高的概率重建原始图像或高维信号。
Objective Due to random sampling of non-adaptive,high-quality reconstruction of the original signal,one-dimensional non-stationary multi-channel EEG signal can be achieved automatic detection and analysis.Methods A new multi-component redundant dictionaries with the atoms of the Gaussian function and its first and second derivatives was built in the paper,and reconstructed signal base on compressed sensing measurement model.Results The selected dictionary atoms can more effectively match the EEG signals in a variety of transient characteristics of the waveform,allowing the formation of EEG signal is more sparse matching pursuit decomposition.With the theory based on compressed sensing signal sampling,only half of the original signal with different sample size can be used to reconstruct the original signal quality,the important instantaneous features of the waveform can well be maintained.Conclusion Signal sampling based on the theory of compressed sensing contains enough information of the original signal,using the prior conditions of EEG signals（or compressibility）sparsity,high-dimensional signal and original image can be reconstructed through a certain decoding of linear or nonlinear model.
出处
《中国医疗器械杂志》
CAS
2010年第4期241-245,共5页
Chinese Journal of Medical Instrumentation
基金
国家高技术研究发展(863)计划(2007AA12E100)
国家自然科学基金资助项目(60802039
60672074)
教育部高校博士点专项科研基金(20070288050
M200606018)
江苏省研究生创新基金
关键词
压缩感知
棘波检测
多成份字典
稀疏逼近误差
稀疏表示
匹配追踪
Compressive Sensing
spike-wave detection
multi-component dictionary
normalised mean square error
sparse representation
matching pursuit
