Bispectrum Analysis Method Based On Matlab Heart Sound Signal

The cardiovascular disease is one of the diseases threatening the human health seriously. All the time, the doctor and project personnel pursue early diagnosis ability of cardiovascular disease. And the heart sound signal analyses are very useful in medical science. Heart sound signal is a compound sound of blood fluxion in heart systolic and diastole phase. It is a kind of basic method to appraise heart function state, and includes the physiology and pathology information of each part and interaction on in heart.The research of the heart sound signal has been a noticeable subject all the time. At heart sound analytical method, it has time analysis, frequently analysis, and the power spectrum analysis previously. now it goes into the time-frequency analysis. The typical methods include STFT, auto-regression model and the wavelet in the time-frequency analysis of the heart sound. In this paper We adopts the wavelet to analyze heart sound signal and discusses the other methods to compare the signal analysis results.In recent years, some new methods have been developed to process non-stationary signals, but the results are not satisfied. Therefore, N. E. Huang et al in NASA proposed Hilbert-Huang transform (HHT) in 1998, which is a kind of new time-frequency analysis method for nonlinear and non-stationary signals. The key part of the method is the empirical mode decomposition (EMD) method with which any complicated data set can be decomposed into a finite and often-small number of intrinsic mode functions (IMF) that admit well-behaved Hilbert transforms. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and non-stationary processes. With the Hilbert transform, the IMF yields instantaneous frequencies and the amplitude functions. The final presentation of the results is an energy-frequency-time distribution, designated as the Hilbert spectrum.Bispecturm analysis is one of the useful tools for the non-linear systems, non-Gaussian processes and nonminimum phase, and was applied in many fields, such as radar, sonar, physical geography, biomedicine, image reconstruction, mechanical fault diagnosis, harmonic retrieval, array processing etc. However, bispectrum has the limitation, for instance, it is incapable of avoiding the disturbance of non-Gaussian noise. In addition the definition of bispectrum is on the basis of the random stationary signals, but in fact, a lot of time-varying signals are obtained, so its application is restricted to great extent. Based on the deficiency of traditional bispectrum, bispectrum analysis is developed. The theory is developed and perfected, and applied to mechanical fault diagnosis. Following are the primary contributions:(1) In the First, exposition of some conventional non-stationary signal processing methods were discussed, then introducing the high-end spectrum analysis in the development of signal processing of the status at abroad as well as heart sound signal on the status, and describing the prospects for development of the heart sound signal.(2) The definition, character, algorithm and physical bispectrum analysis were introduced, then the deficiency are pointed out. meaning of traditional analyzed, and the start and the necessity of further study are pointed out.(3) Based on the deficiency of traditional bispectrum, which has good insensitivity to independent Gaussian noise. However, it is incapable of avoiding the disturbance of non-Gaussian noise, Combining wavelet transform with bispectrum, a new method based on bispectrum analysis in the wavelet transform domain is proposed, and applied in the diagnosis of heart sound signal. The experiment results show that the proposed method is superior to the traditional bispectrum, especially in the exist of non-Gaussian noise.(4) The choice of threshold in the wavelet standard, so the denoising threshold the application of wavelet threshold is chose threshold denoising has no the unitive through the subject or experience, and denoising is blocked. Based on it, the method based on Hilbert-Huang Transform bispectrum analysis was introduced, Through experience mode (EMD) decomposition of the signal decomposed into a series of intrinsic mode function (IMF), Hilbert-Huang transforming all IMF, the signal will be used to bispectrum analysis. The experimental results show this method is a good inhibition in the exist Gaussian noise.