New Method Of EEG Rhythm Extraction And Research Of Automatic Sleep Stage Classification

Sleep is a very important life processes and human physiological activities. It is very close relations with human health, learning, living and working. But human poorly understood it yet. Insomnia is one of the most common sleep disorders. Insomnia is a sleep the origin and maintenance of barriers, resulting in sleep quality and quantity can’t meet the individual physical needs. And patients have a significant adverse impact on daytime activities, objective detection of abnormal syndrome. EEG is the overall reflection of the electrical activity of brain cells in the cerebral cortex or the scalp surface. It is an important basis for the study of brain function and brain disease diagnosis, EEG contains a very large amount of information. EEG-based sleep staging played a very important role in the treatment of insomnia.EEG is a time-varying and non-stationary signal, different times with different frequency components. Therefore, the EEG extraction has become an integral part of EEG processing. This article describes the background knowledge and EEG analysis techniques Research. It describes traditional wavelet and the second generation wavelet transfer theory, as well as their advantages and disadvantages in the EEG rhythm extraction. It sets out the lifting scheme algorithm theory and algorithm description, completed the DB4 wavelet enhancement. DB4 wavelet lifting algorithm is used for the EEG rhythm extraction. Sleep staging rules and the characteristics of sleep stage are introduced.In the Hilbert-Huang transform, intrinsic mode functions obtains through empirical mode decomposition. Then we can find the corresponding instantaneous frequency and study its physical significance.The experiments show that the DB4 wavelet lifting scheme can extract high-quality 8 and θ rhythms. The results prove that the wavelet lifting scheme is feasible in the EEG rhythm extraction. High-quality 8 and 6 rhythms provide a guarantee to induce insomnia patients with sleep, and achieve the goal of treatment of insomnia.In the Hilbert-Huang transform, intrinsic mode functions obtained through empirical mode decomposition. We can obtain a physically meaningful instantaneous frequency, amplitude spectrum and the energy distribution of the EEG frequency. The energy distribution on the frequency is characteristics of the sleep EEG. Sleep EEG features can be used as an effective basis for sleep stages. One hundred and twenty samples were picked from sleep EEG, and classification was made. The mean rate of accuracy was as high as 82.49%.