The Adaptive Noise Canceller Based On The Abstraction Of Fetal Heart Rate

1.IntroductionFetal Heart Rate signal is an objective index which reflects the physiological activity of foetus heart electricity, It signifies an important life parameter that the life entity exists or not, it is a kind of typical biomedical signal, belong to the non- steady random signal. For dealing with the signal, Wiener filters and Kalman filters restrain, they demand to know in advance that the priori of the signal count the characteristic, The adaptive filtering theory and technology is main content that count signal processing and non-steady random signal processing, it has the optimum filtering performance of Wiener filters and Kalman filters. The adaptive filtering may be under the condition of the signal unknown its statistics characteristics, approach gradually the optimum filtering result and adapt to the change of the random environment of outside through self-adaptation. This paper makes a research on various kinds of the adaptive noise canceling algorithms. In the end the author proposed the algorithm of clipped-error LMS algorithm with variable step size of segmenting type. And it was applied the system of the adaptive noise canceling, thus it realized the abstraction of the signal of Fetal Heart Rate.2.The research on the adaptive noise canceling and proposed a new algorithmThis paper regards the adaptive filtering principle as the foundation at first, using the system of the adaptive noise canceling, to the algorithm of Normalized LMS, Modified LMS and clipped-data and clipped-error LMS and some other improved LMS algorithms with variable step size , As to the specific same signal , through experiment , the author compared and analysed on the degree of improving SNR and the noise filtering result degree, drawing that the clipped-error LMS algorithm with variable step size and the clipped-error LMS algorithm may reach best noise canceling compared with other algorithms, SNR has improved 11dB. But because of the clipped-error LMS with variable step size adopting variable step size type, its operation time is not shortest compared with the clipped-error LMS algorithm, this proves that the operation time of the algorithm is the least minimum too, this is unanimous with the argumentation in theory .Clipped-error LMS algorithm reduces the calculating amount, but at the convergence rate and step the algorithm of LMS with variable step size for Sigmoid function is more slowly compared with other algorithms. This needs us to explore the better algorithm or improve the clipped-error LMS, in order to realize that the algorithm can accelerate the convergence rate as well as improving the operation time.On the basis of the discussion above, this paper has put forward two kinds of new algorithms, On the basis of the discussion above, this text has put forward two kinds of new algorithms, LMS algorithm with variable step size of segmenting type and the clipped-error LMS with variable step size of segmenting type separately. Through comparing the two algorithms all-round on the improving degree and convergence rate, the author drew a conclusion that the clipped-error LMS algorithms with variable step size of segmenting type can reach better result on resuming wave, but the clipped-error LMS algorithm with variable step size of segmenting type is the most ideal algorithm on the adaptive noise canceling. The author proposed the clipped-error LMS algorithm with variable step size of segmenting type, which is based on the criterion of minimum square error. The kind of algorithms is proposed based on LMS algorithm with variable step size of segmenting type and clipped-error LMS algorithm. LMS algorithm with variable step size of segmenting type deals with the number of iterations by step, at the first adapting step, the author adopts greater step to make fast convergence, with the going on of adaptive course, selecting less step value gradually. Thus the step changes lighter and lighter after it enters steady stage, in order to get the misadjustment value as little as possible . Expressed the number of...