The Value Of Resting State Electroencephalography In Diagnosis Alzheimer’s Disease

Background and Objective:Alzheimer’s disease(AD)is a degenerative disease of the nervous system and is the most common type of dementia.accounting for about 70%of all dementia cases,which brings a heavy burden to families and society,so early diagnosis is particularly important.At present,neuroimaging and cerebrospinal fluid markers are more popular for the diagnosis of Alzheimer’s disease.However,they cannot be widely used in clinical practice due to high cost and invasive.Electroencephalogram(EEG)has emerged as anon-invasive alternative technique to study AD,but it remains a challenge to predict AD.Therefore,the purpose of my project is to study the value of resting-state EEG markers in the diagnosis of AD and to use EEG tools to objectively distinguish between normal individuals and patients with Alzheimer’s disease,reducing the rate of clinical missed diagnoses.Methods:59 patients with Alzheimer’s disease and 54 healthy control subjects were enrolled in this study from June 2019 to June 2022 in the Department of Geriatric Disorders,Shenzhen Kangning Hospital.Resting-state EEG data were collected in patients within 1 week after admission.They were evaluated MMSE,Mo CA and CDR scales.Based on SPSS25.0 software,the chi square test,rank-sum test and two-sample T-test were used to compare the general situation and clinical scales in this sudy.In addition,we use EEGLAB upgrade toolkit in running under MATLAB R2018b environment achieve delta(1-4 Hz),theta(4-8 Hz),alpha(8-13 Hz)and beta(14-30 Hz)power band.Stratified analysis of gender,age and EEG relationship.Power Spectrum Analysis was used to examine the distribution of each EEG variable in the dementia and health control groups.Pearson correlation coefficient and single factor linear regression analysis were used to study the relationship between cognitive and EEG.In addition,logistic regression models to predict dementia were built based on selected EEG variables and others,finally,ROC curves were drawn to evaluate different models performance.Results:(1)The age of Alzheimer’s disease patients was generally higher than that of healthy elderly people(P<0.001).There was no significant difference in gender and education level between the two groups(P>0.05).(2)The scores of MMSE(t=-9.1,P<0.001)and Mo CA(t=-9.9,P<0.001)in the AD group were lower than those in the HC group.The number of patients with hypertension,diabetes,sleep disorders and heart disease in the AD group was more(P<0.05),and rates of brain atrophy were significantly higher in patients with Alzheimer’s disease than in healthy individuals(x~2=20.5,P<0.001).(3)Statistical analysis showed that the power differences between the two groups existed in two frequency bands in some frontal and temporal regions.Alzheimer’s disease patients were characterized by significantly higher theta absolute power values in the right frontal(F4),left and right temporal(F7,F8)regions(P<0.05)and significantly lower alpha/theta absolute power ratio in all frontal regions(Fp1,Fp2,F3,F4,F7,F8)(P<0.05).(4)EEG were weakly to moderately correlated with cognitive function.Delta and theta average power in the right frontal(P4),anterior temporal(F7)and right anterior temporal(F8)regions showed weak to moderate negative correlations with MMSE overall scores,and the alpha/theta absolute power ratio was significantly lower(P<0.05)in all measured brain regions;In the frontal regions(FP1,FP2),alpha,beta,delta average power was weakly to moderately negatively correlated with MOCA scores.(5)In the model analysis,Hypertension,diabetes and brain atrophy,and history of heart disease can increase the risk of Alzheimer’s disease.The average power of alpha、beta、theta,delta and alpha/theta predicted Alzheimer’s disease with good specificity under normal conditions.The mean theta and alpha/theta power were significantly different after Integrating variables.(6)Model analysis found that The combination of EEG predicted ad with a model ROC curve AUC was 88.2%,sensitivity was 96.6%,and specificity was 67.3%,which was higher than any single EEG model.The model with the combination of combined variables had the strongest predictive power,with an AUC of the ROC curve was 94.6%,sensitivity was 91.4%,and specificity was 85.5%.Conclusion:(1)Alzheimer’s disease present with EEG abnormalities characterized by increased theta power and decreased alpha/theta in parts of the frontal and temporal regions,and correlates with cognitive function.(2)Resting state EEG markers have higher evaluation in diagnosing Alzheimer’s disease.