Alzheimer's disease: Models of computation and analysis of EEGs

被引:116
作者
Adeli, H
Ghosh-Dastidar, S
Dadmehr, N
机构
[1] Ohio State Univ, Dept Biomed Informat, Columbus, OH 43210 USA
[2] Ohio State Univ, Dept Civil & Environm Engn & Geodet Sci, Columbus, OH 43210 USA
[3] Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA
[4] Ohio State Univ, Ctr Biomed Engn, Dept Neurosci, Columbus, OH 43210 USA
[5] Ohio State Univ, Dept Neurol, Columbus, OH 43210 USA
关键词
Alzheimer's disease; chaos; EEG; neuroscience; time-frequency; wavelet;
D O I
10.1177/155005940503600303
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
In a recent article the authors presented a comprehensive review of research performed on computational modeling of Alzheimer's disease (AD) and its markers with a focus on computer imaging, classification models, connectionist neural models, and biophysical neural models. The popularity of imaging techniques for detection and diagnosis of possible AD stems from the relative ease with which neurological markers can be converted to visual markers. However, due to the expense of specialized experts and equipment involved in the use of imaging techniques, a subject of significant research interest is detecting markers in EEGs obtained from AD patients. In this article, the authors present a state-of-the-art review of models of computation and analysis of EEGs for diagnosis and detection of AD. This review covers three areas: time-frequency analysis, wavelet analysis, and chaos analysis. The vast number of physiological parameters involved in the poorly understood processes responsible for AD yields a large combination of parameters that can be manipulated and studied. A combination of parameters from different investigation modalities seems to be more effective in increasing the accuracy of detection and diagnosis.
引用
收藏
页码:131 / 140
页数:10
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