Research Of Blind Source Separation Algorithm Based On Independent Component Analysis

Blind source separation have been widely appreciated in many fields, especially in the medical signal analysis and processing, geophysical data processing, data mining, speech enhancement, image recognition and wireless communication. We make use of multi-electrode device to obtain biomedical signals which is successful and great development potential application in blind signal processing. Therefore, the practical application of blind source separation has become a major trend. With the development of blind source separation, independent component analysis is a developing multi-dimensional signal processing technology in recent years. ICA which is an effective processing method, can effectively denoising and dig out more useful information. The major research work as follows:1. This paper introduces the basic theory, models, optimization algorithms, optimization criterion of independent component analysis and discusses the main criterion for ICA which includes the maximum likelihood estimation, information maximization, mutual information minimization and non-Gaussian measure. The main algorithm includes:Joint approximate diagonalization method (JADE), fast fixed-point iteration method (FastICA), information maximization (Infomax). Compared the performance of ICA algorithms, we select the improved algorithm to separate EEG signals and extract ECG signals.2. Symmetric orthogonalization FastICA algorithm overcome the problems that Gram-Schmidt orthogonalization FastICA algorithm estimation error has been accumulated in subsequent vector and guarantee the extracted for each letter of the source had not yet been extracted and rule out the weight of the extracted. The feasibility and effectiveness of the algorithm are confirmed by the result of Simulation experiments. The comparison between the two algorithms'results proves that the symmetric orthogonalization FastICA algorithm can effectively remove artifacts of EEG and is more feasible in the practical application.3. ECG signals which measured from the pregnant women's and with unknown parameters of transmission medium, actually contain respective ECG signals of pregnant woman and fetus. We make use of the independent component analysis to extract ECG signals and provide accurate data for diagnose. This paper proposed a Joint algorithm in order to avoid the defects of SOBI algorithm and JADE algorithm. This algorithm which has a good stability utilization of the advantages of the two algorithms can not only clearly extract the fetus of ECG and significantly reduce noise disturbance and human intervention.