独立分量分析在医学信号处理中的应用

【作者】 赵浩；

【导师】 周卫东；

【作者基本信息】 山东大学 ， 通信与信息系统， 2005， 硕士

【摘要】 在医学信号处理领域,信号中常常不可避免的混叠有不同种类的干扰成分,如通道间的串扰、眼电、心电、肌电、工频干扰等。这些噪声和干扰来自于一些独立的生理源或者噪声源。如何从采集的原始信号中获取能够反映身体机理的有用信息,始终是一个比较棘手的问题。 独立分量分析(Independent Component Analysis)是基于信号高阶统计量的一种信号处理方法,其基本含义是将多道观测信号按照统计独立的原则通过优化算法分解为若干独立成分,其前提条件是各个源信号为彼此统计独立的非高斯信号。自从1994年Comon系统地分析了瞬时混叠信号盲源分离问题,并提出了ICA的概念与基本假设条件之后,对ICA的研究日益加深,引起包括生物医学领域在内的众多研究人员的关注。实践证明,ICA方法能够用于包括脑电信号在内的生物医学信号的噪声消除,这是对传统处理手段的一种突破。此外,ICA的空间滤波特性不受信号频谱混叠的限制,在消除噪声的同时内能够很好的保留有用信号的细节成分,很大程度上弥补了时频域方法的不足。本文研究了独立分量分析的部分理论,并在医学信号方面对ICA进行了部分应用研究,这在临床医学和脑科学认知领域具有一定的意义。 本文介绍了独立分量分析的模型、优化判据和寻优算法,讨论了ICA的主要判据,包括非高斯性度量、互信息最小化、信息极大化及极大似然估计,论述了FastICA算法和InfoMax算法,并编写了Matlab仿真程序对医学图像和脑电信号进行了分析处理。本文在研究ICA的基础上,系统研究了盲分离效果评判的指标,对行元素占优指标、信噪比、相关系数和相似系数、峭度、近似熵和相平面进行了讨论,并对行元素占优指标进行了改进。 本文对Noisy ICA进行了初步研究,并结合小波阈值去噪分析方法,将FastICA算法应用于噪声混合图像分离,对独立分量分析在噪声环境下的应用方法进行了仿真研究。结果表明:对于含有传感器噪声的混合信号,无论噪声的强度如何,先对观测信号进行去噪,再进行独立分量分析,能有效的提升分离结果的峰值信噪比,效果良好。更多还原

【Abstract】 In the field of medical signals processing, the signals are corrupted by all kinds of artifacts, such as the power line influence, Electro-Oculogram (EOG), Electrocardiograph (ECG). All these artifacts are from mutually independent sources. How to extract the useful components from the corrupted raw data still remains a challenging problem.Independent Component Analysis (ICA) emerged as a novel technique for blind source separation. This method is based on high order statistics. ICA is to separate the original signals into several independent components by selecting a particular criterion and an optimal algorithm. Though the specific information of the sources and mixing system are unknown, the probability for the source decomposition can be realized by using the principle of statistical independence. ICA can not only remove the correlation between signals, but also make use of the higher order statistics. Therefore, ICA has a better performance than Principal Component Analysis (PCA) and is widely used in many fields.The definition and principles of ICA was presented firstly in this paper. It mainly focuses on the application of ICA in the filed of biomedical signal processing. The results of this study can contribute to the clinical application and the cognitive sciences and brain science. In this article we adopt the algorithm of Fast ICA and Infomax ICA for image separation and EEG artifact reduction in the Matlab environment.In this paper, the ICA of noisy signals is discussed. In the simulation, the technique of wavelet threshold de-noising and the algorithm of Fast ICA are both studied with computer simulation of noisy image separation. The simulation results show that for the mixed images with additive white Gaussian noise, it’s better to de-noise the images before applying ICA than to apply ICA first and then de-noisethe independent components.We also combine the AR model with the ICA model to analyze and classify the mental EEG with BP neural network. We first purify the raw mental EEG signals using ICA, and the artifacts and noises of EOG and power line interface are removed. After ICA is applied to extract the ICs of the cleaned EEG signals, the AR model is used to calculate the AR coefficients. Finally, the BP neural network was trained to classify the signals. The testing simulation shows that the method can get a recognition rate of 80 - 90%.更多还原