建立以腦波為基礎的阿茲海默病評估模式

Translated title of the contribution: Clinical EEG-Based Assessment Model for Alzheimer's Disease

Chih-Chung Chen, Hung-Wen Chiu, Chien-Yeh Hsu

Research output: Contribution to journalArticle

Abstract

Background
Typical EEG findings of Alzheimer's disease are increase of slow waves and decrease of fast waves. These changes are not specific for this disease, so it is commonly regarded EEG as of limited value for disease diagnosis. Long-term clinical observation suggests that the degree of these EEG changes seemed to be associated with the severity of the disease, and EEG study may serve as a potential tool to help clinical follow-up of these patients. We conducted this study to find sensitive and useful EEG parameters and provide a applicable model for the clinical assessment of Alzheimer's disease.
Method
This retrospective study included three medical institutes and fifty-nine patients of Alzheimer's disease. Based on spectral analysis, we calculated and presented power ratio and interhemispheric alpha coherence of individual EEG. We grouped the patients as very mild、mild、moderate、and severe groups according to the information of CDR or GDS and analyzed the correlation of these EEG parameters and different groups.
Result
Mean power ratio of each group is 0.72 in very mild group、1.17 in mild group、1.86 in moderate group and 3.00 in severe group, respectively. Higher value suggest more severe disease. Interhemispheric alpha coherence showed diffuse decrease as the advance of disease. Among the electrode pairs, P3-P4 showed better correlation with the disease severity. Mean alpha coherence in each group is 0.474 in very mild group、0.415 in mild group、0.347 in moderate group and 0.271 in severe group, respectively. Lower value of P3-P4 alpha coherence suggest more severe disease. According to ROC curve, cutoff value among groups were suggested. Correct classification was 62.7% and 52.5% by power ratio and P3-P4 alpha coherence respectively.
Conclusion
EEG is a objective diagnostic tool, suitable of providing information of severity of Alzheimer's disease. Among various parameters, we recommended power ratio and P3-P4 alpha coherence as the tools to assess Alzheimer's disease. Although not sensitive enough to compare with clinical assessment scales, such as CDR or GDS, it can be a objective tool to supplement current assessment method. Our study provide a practical model that can be widely applied among institutes to follow up these patients.
Translated title of the contributionClinical EEG-Based Assessment Model for Alzheimer's Disease
Original languageTraditional Chinese
Pages (from-to)43-52
Number of pages10
Journal醫療資訊雜誌
Volume16
Issue number2
Publication statusPublished - 2007

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