A Wavelet-Chaos Methodology For Analysis Of EEGs To Detect Epilepsy

Epilepsy is a common brain disorder that affects about 1%of the population in the world and is characterized by intermittent abnormal firing of neurons in the brain. Brain activity in the ictal state differs significantly from the activity in the normal state with respect to frequency and pattern of neuronal firing. Nevertheless, detection of seizures can be challenging even from a visual inspection of the EEG by a trained neurologist for a variety of reasons such as excessive presence of myogenic artifacts. Prediction of epilepsy is even more challenging because there is very little confirmed knowledge of the exact mechanism responsible for the epilepsy. Effective algorithms for automatic epilepsy detection and prediction can have a far reaching impact on diagnosis and treatment of epilepsy.There were someone before who have studied the nonlinear dynamics of EEG data in epilepsy patients, and in their subsequent studies concluded that the chaos in the brain was reduced in the phase before the epilepsy. After that some researchers performed a quantitative comparison of EEGs corresponding to normal and epileptic brain activity using fractal analysis. They almost proved the reduction in the phase before the epilepsy. This paper just based on this, and proved the conclusion in the research.EEG is a signal that represents the effect of the superimposition of diverse processes in the brain. Almost all of the present EEG processing researches have been done based on primitive and entire EEG, Little research has been done to separately study the effects of these individual processes. The characteristic difference of normal EEG and epilepsy EEG is not obvious in the reaserches based on primitive EEG. That brings the difficulty for epilepsy's forecast and diagnosis.Each EEG is commonly decomposed into five EEG sub-bands:delta(0- 4Hz), theta(4-8Hz), alpha(8-12Hz), beta(13-30Hz), and gamma(30-60). There is no good reason why the entire EEG should be more representative of brain dynamics than the individual frequency sub-bands. On the contrary, the sub-bands may yield more accurate information about constituent neuronal activities underlying the EEG and consequently, certain changes in the EEGs that are not evident in the original full-spectrum EEG may be amplified when each sub-band is analyzed separately. This is a premise of this paper.In this paper a wavelet-chaos methodology is presented for analysis of EEGs and EEG sub-bands for detection of epilepsy. It consists of three stages: (1)wavelet analysis, mainly decompose the EEG into five sub-bands using the wavelet filter. (2)preliminary chaos analysis, in this phase finish creating of lagged phase space and get the optimum lag and the minimum embedding dimension.(3)final chaos analysis, get the correlation dimension and the largest Lyapunov exponent on the base of last phase.In the former EEG processing methods, most of the methods only analysis the EEG with chaos, and were only interested with the correlation dimension. Because it is very difficult to distinguish the epilepsy EEG from the normal EEG. But this paper has made an analysis of the EEG sub-bands, and has calculated the correlation dimension and the largest Lyapunov exponent of each sub-bands of the epilepsy EEG and the normal EEG. Through the analysis, this paper has found that there were some information in the sub-bands but not obvious in the EEG, and based on this, it is more accurate to distinguish epilepsy EEG from the normal ones.This paper makes great effort to develop the EEG processing methods, combines the wavelet and chaos, through analyzing the EEG sub-bands, and then make the detection of epilepsy more accurate.But because of the complexity of the EEG and usually adulterate with much noise, it is very difficult and challenging to process and analyze. In fact, until today, there is none uniform criterion and conclusion to detect especially predict the epilepsy. And the wavelet-chaos method is just finding the more accurate conclusion, and wishes that this is a foundation for the following research.