Research On Independent Component Analysis And Its Application In Electrocardiography And Pudendal Evoked Potential Signal Processing

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.