The FECG Detection Algorithm And Implementation Based On BSS

Fetal electrocardiograms (FECG) conclude much information of fetal health as maternal electrocardiograms do. By analysising the FECG we can discovery if fetus are in the condition of embarrassment or lack of oxygen as well as cord entanglement, and take some treatment in timely. So, extracting clear FECG becomes very significant. But disturbance of the maternal relevant noises and the weakness of the signal makes the extraction of the FECG becoming a spot problem in the signal processing area.Independent component analysis (ICA) is a newly developed method of blind source separation. It has been widely used in the separation of FECG recent years. According to ICA theory,the number of observing signals must be larger than that of independent components. This makes the use of ICA theory limited in clinical monitoring. So this thesis develops an improved algorithm, the main contents are as fallows:(1)Against the limitation at the number of signals, we propose an algorithm that extracts the FECG from few channels of observing signals: combining ICA and adaptive noise cancellation separate FECG from two leads of maternal ECGs.(2)Using the Mexican-hat wavelet transform method to detect QRS complex:Locate the R peak using wavelet threshold and detect the Q, S peaks using the plane geometry method.(3)Design a FECG detecting system using Visual C++, showing the real time ECGs recorded from maternal surface, and at the same time, extract the FECG instantaneously. The users can on the one hand observe the situation of the FECG, on the other hand contrast the FECG with that saved in the system before.