Analysis Methods Of Eeg Signals With Mild Cognitive Impairment Patients

Mild cognitive impairment (MCI), which is an early stage of senile dementia, hasbecome the current concerns of the early diagnosis of senile dementia. The specificcoupling and synchronization attribute of EEG signals from MCI patients provide apossibility for us evaluating and diagnosing MCI. It also becomes a new approach toevaluation and diagnosis to MCI that trace the EEG signals of scalp to areas of intracranialcortex, and analyzes the functional connectivity between two areas of the cortex.Therefore, this dissertation introduces the permutation conditional mutual informationmethod and improves the statistical approach of coupling directionality index in thismethod, on account of the most current methods for analyzing EEG signals of MCI do notconsider coupling direction and the methods considering coupling direction lack theeffective statistics of coupling direction. To improve the computational accuracy of thecoupling between EEG signals of two channels in the method of multi-channel globalsynchronization strength, the dissertation propose the new method of global couplingindex, evaluate the MCI using the method, and compare this method with two existingmethods. And this dissertation used sLORETA software to trace to the source of scalpEEG signals of MCI patients and study the function connectivity between two corticalareas. This dissertation compared and analyzed the performance of coupling,synchronization, connection analysis of intracranial cortex and theses combinationmethods.Firstly, the dissertation explains in detail the basic knowledge of EEG signalsanalysis used for evaluating MCI. In the dissertation, a few common neuropsychologyscales used for evaluating MCI was introduced, the basic collection methods andpreprocessing methods of EEG signals were stated, the basic features of EEG signals ofMCI are analyzed. Then, this dissertation expound the common methods of coupling andsynchronization for the EEG signals of two channels from MCI, explained in detailexisting methods of coupling and synchronization for the EEG signals of multi-channelsfrom MCI, and described the source localization principle of sLORETA and the calculatedprocedure of functional connection based on sLORETA.Secondly, this dissertation introduces the method of permutation conditional mutual information, improve the statistical approach of coupling directionality index, quantize thecoupling strength and direction of two resting-state EEG signals from different frequencybands and different encephalic regions of aMCI(amnestic Mild Cognitive Impairment) andcontrol in T2DM (Type2Diabetes Mellitus). And calculate the difference betweencoupling strength and the difference between coupling direction of two groups of subjects,also analyze the correlation between the scores of neuropsychology scales of all subjectsand their corresponding coupling strength or coupling direction. The results displayed thatthere exist differences between coupling strength or direction of EEG signals from manybrain regions group of aMCI and control in Alpha1and Alpha2frequency bands.Thirdly, the dissertation proposed a new method named global coupling index, inorder to estimate accurately the synchronization strength of multi-channel EEG signalsfrom MCI patients. This dissertation uses the simulated EEG signals to comparerespectively the influence of frequency bands, coupling coefficient, and SNR to globalcoupling index and two traditional methods including global synchronization index and Sestimator. Then used the three methods to analyze and compare the global synchronizationstrength of multi-channels EEG signals from MCI and control, also analyze the correlationbetween the global synchronization strength and the scores from neuropsychology scalesof all subjects. The results showed that the synchronization strength based on GCI be lessaffected by the change of frequency bands relative to the other two methods, there existmore excellent performance on GCI method according to the change of couplingcoefficient versus GSI and S estimator, and GCI is more sensitive than GSI andS-estimator on distinguishing the synchronization strength of EEG signals from MCI andNormal Control, especially in the Alpha frequency band.Fourthly, the dissertation analyzes the source of scalp EEG signals of aMCI in T2DMby using sLORETA software, and functional connectivity between different regions of thecortex. And calculated the average current density of regions of cortex from different typesof samples with sLORETA, analyzed the correlation between average current density ofdifferent regions of cortex in various frequency bands, in which the calculated results ofcorrelation coefficient represent the strength of functional connection. The resultsdisplayed the functional connection strength of many regions of cortex group were less in aMCI than control on the Delta,Alpha2and Gamma frequency band, and the functionalconnection strength of other regions of cortex group were more in aMCI than control onthe Delta, Theta, Alpha1, Alpha2,Beta1and Beta2frequency bands.Fifthly, considering the method selective demand of clinical researchers and clinicianin evaluating MCI, this dissertation compared the performance of PCMI, PCMI based onsLORETA preprocessing, GCI, GCI based on sLORETA preprocessing, sLORETA. Theresults shown that PCMI and GCI are better than PCMI and GCI with preprocessing ofsLORETA; PCMI, sLORETA and PCMI are superior to GCI, sLORETA and GCI; PCMIis best among the5methods for calculating the coupling strength of local brain areas andestimating the direction of information flow between two brain regions, and it may act asthe first choice evaluating MCI; GCI may become the preferred method when we analyzethe multi-channel EEG signals of MCI.In general, this dissertation studied the methods of evaluating MCI from twoperspectives and three levels, of which the two perspectives include the analysis of scalpEEG signals and analysis of cortex EEG signals, the three levels incorporate the analysisof EEG signals for evaluating MCI, the analysis of EEG signals in scalp and cortex ofMCI, the analysis of two-channel and multi-channel EEG signals and the source EEGsignals from cortex based on sLORETA. Then the dissertation compared and analyzed theperformance of multiple methods on the third level, in order to find the best analysismethod evaluating MCI.