Analysis And Research On Time-frequency Characteristics Of Nonlinear Snore Signal

Based on the time-frequency properties of the breathing signal in the normal weak physiologiacal medicine, the relevance of their occurrence mechanism and propagation characteristic of clinacal physiopathology are researched, at the same time, their effects on the normal medical signal and nonlinear characteristic are analysed. In order to explore the different time-frequency feature of snoring signal, the snoring identification model are set up. In this paper, the primary works are outlined as follows:(1) The mechanism of production on the normal snore and pathological sleep apnea syndrome are studied. First, the production mechanism of both are different in the medical science. When he is sleeping, all of the muscle are relaxing. The normal snore signal is formed from the vibration of resonator if nose and pharynx or throat have been obstructed and the air go throuth the narrow parts. Sleep apnea is formed from shaking of the pharyngeal soft palate and nasal breathing air blows, in addition to the loudness beyond60db, there are often accompanied by varying degrees of obstructive sleep apnea syndrome. Second, model analysis system is established from anatomy, the result shows that the different obstructive position may cause different sound loudness and the damage to the body is different. The heavier airway obstruction, the greater the noise is, namely the purring sound is ringing.(2) The detection analysis mothods of the snoring signal are studied. It exists many analysis methods include time domain and spectral analysis and time-frequency analysis in the signal processing. It is discussed that the wavelet transform applied to the snoring signal with nonlinear characteristic of biomedicine signal and time-frequency method. It is difficult to dispose of this weak physiological signal with background noise. First, signal denoising are analysed using the wavelet entropy principle. Second, the wavelet transform is applied to detect the signal singularity. Third, it mainly uses the Hilbert Huang spectral analysis method to analyze the measured snoring and extract its characteristic parameters. Through the comparative analysis, as a new kind of multi-resolution analysis of wavelet transform method, it can be analyzed both in time and frequency domains at the same time. Therefore it is suitable for processing snore signal.(3) The time-frequency domain characteristics of different snoring signals are analysed and compared. It is important to mode recognition of the signal. First, it is to identify the differences and similarities between normal snoring and sleep apnea pathology according to the medical diagnosis standard. Second, by extracting the characteristic value of breath sounds (such as the average energy, cepstrum, AR model coefficient and characteristic parameters, etc.). It using the method of pattern recognition to snore signal classification. Third, it is considered that the many parameters about obstructive pathological snoring and normal snoring signal, such as fundamental frequency, loudness, formant frequency and time interval of acoustic. And pathological signal information and microscopic characteristics and snoring signal model analysis system are researched. The result shows that different signal's time and frequency characteristic are different and the signal simulation models are different.(4) The analog simulation system of different signal are studied. The database is set up according to signal distributional and classify. Adopting Matlab7.X programming language and using Matlab and Excel dynamic link,it is realized the snoring signal analysis of the data storage and statistics. Adopted a sonogram analysis and non-stationary sounds signal dynamic spectrum, model analysis and software characteristic parameters, pathological information from signals, it is provided the basis for medical science in the early diagnosis of sleep apnea.The main contributions of this thesis are as follows:(1) The wavelet transform, as a new snoring signal detection method, is proposed. As complexity of the physiological acoustic signal, weak and nonlinear characteristics, the application of the wavelet entropy of noise removing interference method has achieved good results.(2) Adoped the orthogonal wavelet transform time-frequency analysis method, it is effect to accurate analysis of different time-frequency characteristics of snoring signal and extract the spectral feature parameters in the characterization of the physiological signal pathological features.(3) Snoring sound signal's the subtle features are researched by analyzing simulated acoustic characteristics of the different pathologica apnea signal, it gets accurate detection and signal mutation.(4) Snore signal analog simulation software is designed. The result of the signal modeling through Laplacian and Gaussian distributions is a set of possibilities per sound sample for snore events.