The Analysis And Application Research Of Sleep EEG

Modern medicine consider that sleep is a very important physiological process, sleeping recovers people’s mental and physical;the quality of sleeping are closely related with people’s health, learning, life and work. Insomnia is the most common sleep disorder, although it does not belong to a serious disease, but it influences on people’s health, learning, life and work. EEG is the overall reflection of the electrophysiological activity of brain cells in the cortex or on the surface of the scalp. So study the information which EEG contains and grasp the changing rule of the sleeping cycle, it has important significance to diagnosis and treat the diseases associated with sleeping.Sleeping EEG is a kind of nonlinear and non-stationary signal, it has different frequencies at different instants of time. According to the frequency distribution,the EEG signals mainly covers four different frequency of the wave, including the delta rhythm wave (1-4Hz), theta rhythm wave (4-8Hz), alpha rhythm wave (8-13Hz), beta rhythm wave (13-30Hz). The extraction of rhythm wave in EEG signals, is the indispensable important part in studying the EEG Wavelet transform developed in the late80s is an important tool for signal analysis, and it is a branch of applied mathematics,it is also a new development of Fourier transform. Wavelet transform overcomes the limitations of Fourier transform,it has good localization features both the time domain and frequency domain.This paper mainly introduces the background knowledge about the sleep、the research status and characteristics about EEG signals; And introduces the method of wavelet theory、 the application of wavelet transform based denoising in EEG signal、the application of sleep EEG rhythm extraction based wavelet theory;And this paper expounds the criteria of sleep staging simply、the characteristics of sleep EEG in different periods and introduces the wavelet packet energy spectrum in the application of sleep staging. Experimental results show that the application of wavelet theory can get rid of the noise in EEG signal clearly, and retain the important information which existed in the original signal; At the same time, the application of wavelet decomposition and wavelet packet decomposition can extract the various rhythm wave in EEG signals effectively;Finally,we study the statistical law of the characteristic vector based on wavelet packet energy spectrum,and find that it can separate sleep periods,so we think that it can be used as an important characteristic parameter for sleep staging.Medical scholars have found that the delta rhythm wave and theta rhythm in EEG signals wave played an important role mainly in sleeping and deep sleeping stage. So finally, this paper puts forward a EEG biofeedback therapy method which based on delta and theta rhythm wave to treatment of patients with insomnia.