Alzheimer's disease is a degenerative brain disease that results in cardinal memory deterioration and significant cognitive impairments. The early treatment of Alzheimer's disease can significantly reduce deterioration. Early diagnosis is difficult, and early symptoms are frequently overlooked. While much of the literature focuses on disease detection, the use of electroencephalography (EEG) in Alzheimer's diagnosis has received relatively little attention. This study combines the fuzzy and associative Petri net methodologies to develop a model for the effective and objective detection of Alzheimer's disease. Differences in EEG patterns between normal subjects and Alzheimer patients are used to establish prediction criteria for Alzheimer's disease, potentially providing physicians with a reference for early diagnosis, allowing for early action to delay the disease progression.
['Hsiu-Sen Chiang', 'Shun-Chi Pao']
An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer's Disease