【作者】 郁芸；

【导师】 王惠南；

【作者基本信息】 南京航空航天大学 ， 生物医学工程， 2008， 硕士

【摘要】 独立分量分析(ICA)是近年来由盲源分离(BSS)技术发展来的一种多维信号处理方法。它以非高斯信号为处理对象,在满足一定的条件下,能从多路观测信号中,较完整的分离出隐含其中的若干独立源信号。二十世纪八十年代起,ICA从提出基本概念、研究算法理论,到如今走向应用领域,吸引了越来越多的学者和研究团体,他们从不同的角度对ICA的基本理论、算法、应用进行了深入研究。本文在阐述ICA理论基础的同时,以心电和外阴诱发电位信号的处理为实例,研究了ICA方法在生物医学信号处理中的应用。生物医学信号的产生过程中,包含了人体其他器官组织产生的生物电信号以及各种外界因素引起的干扰信号。我们对其处理的目的是为了从复杂的背景噪声中分离出有用的原始信号,进而从中提取出具有明确生理意义的信号特征,并应用于临床医学的研究。本文所做的主要研究工作如下:1、从基本原理出发,探讨了ICA算法的目标函数和优化算法,阐述了FastICA等算法的基本原理,并对FastICA算法的盲源分离性能进行了仿真验证,结果证明了ICA的有效性。2、研究了ICA在心肌梗塞病人的心电信号分离中的应用,得出了ICA分析方法对于消除心电信号噪声有较好效果的结论;同时将心电信号看成是心电传导通路上不同部位兴奋产生的心电向量的投影,单独研究分离出来的子信号成份有利于获得更加丰富的信息,这可以给临床提供更加灵活多样并且特别的诊断手段。3、研究了ICA在诱发电位信号提取中的应用,提出了将ICA方法应用到外阴诱发电位信号提取中去的方法,为用ICA方法取代叠加平均方法,降低刺激次数,提高受检者的依从性,改善信号提取质量以及该项技术临床应用提供理论基础。更多还原

【Abstract】 Independent component analysis (ICA) is a multi-dimensional signal- processing method which was developed from the Blind Source Separation (BSS) technology in recent years . Under certain conditions , it can separate the multi-channel non-Gaussian observated signals to some hidden independent sources signals .From the 1980s , people has being proposing the basic concept about ICA , researching algorithm theory on ICA , and now ICA comes to the application field . It attracts a growing number of scholars and research groups who study on the ICA’s basic theory , algorithm , application from different angles . In this paper , we expounded basic theory about ICA , and studied it’s applications in biomedical signal processing with Electrocardiography and Pudendal Evoked Potential Signal as examples.During the process of generating Biomedical signals , it mixed biological signals from other human organs and tissues as well as the interference signal from various external factors . The aim of our dealing with it is to isolate the useful original signal from the complex background noise , and extract a clear physiological significance of the signal characteristics , and use it in clinical studies.In this paper , the main research work is as follows:1、Based on the principle , we discussed the goal functions and the optimization algorithm of the ICA , described the basic principles of the algorithm such as FastICA , simulated it and demonstrated the effectiveness of the ICA.2、we studied the ICA’s applications in the ECG signals of patients with myocardial infarction , concluded that the ICA method has a good effects in the elimination of ECG noise . At the same time , we regarded the ECG signal as the Projection of the ECG signal vectors raised from the different excited parts on the transduction pathway . The study of the separated signal components is useful to obtain more abundant information , which can provide a more flexible , diverse and special diagnostic tools for clinical study. 3、We studied the ICA’s applications in the evoked potential signal extraction , proposed the method of the application of extracting Pudendal Evoked Potential Signals , and providing the theoretical basis for alternating the average method with ICA , reducing the frequency of stimulation, increasing the compliance of subjects , improving the quality of the extracted signal , as well as applying for the clinical study.更多还原