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An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer's Disease.

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. Whil...

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Published in:Journal of Medical Systems Vol. 40; no. 5; pp. 1 - 10
Main Authors: Chiang, Hsiu-Sen, Pao, Shun-Chi
Format: equations & formulas research tables/charts Journal Article
Published: Springer Nature May2016
Online Access:View this record in EBSCOhost
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      dt: May2016
      vid: 40
      iid: 5
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      pub: Springer Nature
      place: New York, New York
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        115925335
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        10.1007/s10916-016-0476-7
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        atl: An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer's Disease.
      aug:
        au:
          Chiang, Hsiu-Sen
          Pao, Shun-Chi
        affil: Department of Information Management, National Taichung University of Science and Technology, No. 129, Section 3, Sanmin Road Taichung City 404 Republic of China
      sug:
        subj:
          Electroencephalography
          Alzheimer's Disease Diagnosis
          Early Diagnosis
          Probability
          Early Intervention
          Alzheimer's Disease Therapy
          Disease Progression
          Theory
          Time Factors
          Human
          Brain Waves
          Clinical Assessment Tools
          Male
          Female
          Young Adult
          Adult
          Aged
          Algorithms
          Descriptive Statistics
          Data Analysis Software
          Validity
          Sensitivity and Specificity
          ROC Curve
          Funding Source
          Adult: 19-44 years
          Aged: 65+ years
          Male
          Female
      ab: 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.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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