Analyzing EEG Of Quasi-Brain-Death Based On Approximate Entropy Measures

Brain death diagnosis based on EEG analysis is believed to be valuable for either reducing the risk of brain death diagnosis or preventing mistaken diagnosis. From April 2004 to December 2008, Prof. Cao Jianting with his cooperators, sponsored by JSPS-NSFC's joint scientific cooperative project, collected the EGG data using forehead recording method from 35 patientes with suspected brain death in a hospitial.In our past research, we used independent component analysis (ICA) and complexity to analyze EEG signals that we had recorded 47 sessions of 19 comatose patients and 16 brain deaths. We also used statistical methods to evaluate EEG signals of the two different groups, and concluded that there are significant characteristic differences between the two types of EEG signals. In this thesis, approximate entropy (ApEn) is used to analyze patients' EEG signals. The goal of this thesis is also to study dynamic ApEn that is based on traditional ApEn in order to monitor the state of patient. Since the recorded EEG data was from a real-life environment, therefore it is necessary to introduce a pre-processing technique such as the wavelet technique for the noise reduction. The experimental results illustrate effectiveness of the proposed method in the EEG data analysis and well performance in evaluating the differences between coma patients and brain deaths.