Sleep Eeg Signal Analysis And Processing Methods

The various kinds of brain illness and illness of neural system and social aging make brain science become the most challenging research in the 21st century. It is crucial to decreasing the incidence of brain illness to improve the efficiency of diagnosis in early stage. The sleep EEG is one of the most important methods improving the efficient of the diagnosis.This paper mainly focuses on the filter and rhythm detection using wavelet transform, and sleep depth measurement using Lempel-Ziv complexity. In the thesis, the content is as follow : (1)dissertating the EEG detection and analysis methods inside and outside, in general, (2)to eliminate white noise and disturbances, which include baseline movement, transient pulse interference and muscle disturbance, in EEG using wavelet transform, (3)detecting EEG basic rhythms and epileptic waves based on the multi-resolution analysis of the wavelet transform and singularity detection technology. (4) sleep segmentation using time-window Lempel-Ziv complexity measure, (5)comparing with the common complexity measure. From the result of the simulation, it can be drawn that the time-window complexity measure can helpful distinguish the sleep stages.