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Alzheimer's Disease: Models of Computation and Analysis of EEGs

2005; SAGE Publishing; Volume: 36; Issue: 3 Linguagem: Inglês

10.1177/155005940503600303

2169-5202

Hojjat Adeli, Samanwoy Ghosh‐Dastidar, Nahid Dadmehr,

Fractal and DNA sequence analysis

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.