Characteristic Analysis And Sleep Staging Research Of Sleep EEG

The sleep is a kind of important physiological phenomenon, the electroencephalogram (EEG) is a very important tool for analyzing the sleep. EEG signals have a large amount of physiology and pathology information, electrical activity of the brain plays a very important role in the field of the related disease of sleep and brain science. In this paper, wavelet transform and wavelet packet analysis are used to process the EEG signals, including EEG de-noising and artifact removing. Further, wavelet entropy, approximate entropy, power spectral entropy and complexity measures are calculated.Brain wave is a kind of bioelectricity signal and usually very weak, Its Characteristics is that non-stationarity and low signal-noise rate, so that it is easy to be disturbed by noises, Such as EOG, ECG, EMG and high-frequency noise. For EEG de-noising and artifact removing, this paper presents a threshold de-noising algorithm with the wavelet transform and a adaptive threshold de-noising with the wavelet packet analysis, they can achieve a very good filtering effect.This paper uses the nonlinear dynamics theories of EEG time series including Kc, C0, C1, C2 complexity, power spectral entropy, approximate entropy and waveletentropy. The emphasis is that we analyze and compare the dynamic features and statistical characteristics of the complexity measure for sleep EEG decomposing into four the four basal rhythmbands(δ, θ, α and β) by using orthogonal wavelet transform(WT) andwavelet entropy. We calculate the complexity(Kc, C0, C1), PSE, ApEn and WE of sleep EEG signals. The result shows that the value is the biggest in wake stage, from wake stage to stage S1 and S2 the value become smaller, arid in stage S3 and S4 the value is the smallest, but in REM stage the value is between wake stage and NREM sleep stage,on the contrary, C2 vary oppositely, Especially during deep sleep state C2 increase significantly;...