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

Abstract

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.

Evaluation of spectral ratio measures from spontaneous MEG recordings in patients with Alzheimer's disease

“…In this sense, an enhanced activity in lower frequency bands has been previously observed along with the severity of AD [12,43,44], which is in agreement with the high correlation in the relative power of δ band.…”

“…Some abnormalities in moderate and severe AD patients' EEG and MEG background activity have been observed [11][12][13]. AD patients show a decrease of coherence values at the α and β bands in EEG [14,15], together with a slowing of spontaneous electrical brain activity.…”

EEG spectro-temporal modulation energy: A new feature for automated diagnosis of Alzheimer's disease

“…Moreover, recent research has suggested the limitations of neuroimaging techniques in accurately detecting tangles and plaques [4]. The use of quantitative electroencephalogram (EEG), on the other hand, has shown to be a viable lesscostly alternative, with diagnostic accuracy inline with those obtained with more expensive neuroimaging techniques [5].…”

“…With spectral analysis, the consensus is that EEG spectral power is reduced with AD in the alpha (8)(9)(10)(11)(12) and beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) frequency bands, and increased in the delta (0.1-4 Hz) and theta (4)(5)(6)(7)(8) bands. Spectral coherence (a measure of brain connectivity), in turn, has shown reduced values in AD when studying interhemispheric interactions, and has also been shown to be related to disease progression [6].…”

Towards automated EEG-Based Alzheimer's disease diagnosis using relevance vector machines

“…The most distinctive of these effects include: i) noticeable shift occurring in the EEG power spectrum (e.g., slowing of the EEG) [8]- [10]; ii) reduced inter-hemispherical spectral coherence [11]- [13]; iii) decreased EEG "complexity," likely caused by the reduction in nonlinear connections between cortical regions or even neuronal death [7]; and more recently, iv) a change in neuromodulatory behaviour measured via EEG amplitude modulation analysis [5], [14]. Many of these changes have been shown to be related [15] and diagnostic accuracies in line with more advanced neuroimaging techniques (e.g., fMRI) have been achieved [16].…”