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Review
. 2021 Oct 27:2021:5425569.
doi: 10.1155/2021/5425569. eCollection 2021.

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals

Mahshad Ouchani  1 , Shahriar Gharibzadeh  1 , Mahdieh Jamshidi  1 , Morteza Amini  2   3
Affiliations
Review

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals

Mahshad Ouchani et al. Biomed Res Int. .

Abstract

This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article's purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer's disease, extreme Alzheimer's disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer's disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer's disease science.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Electrode interaction effects caused significant group multiplication in the 8–10 Hz frequency range [20].
Figure 2
Figure 2
For the frontotemporal dementia and control classes, current density images in Talairach space collected by sLORETA were compared [25].
Figure 3
Figure 3
(a) Number of publications about EEG, EMG, fMRI, and PET between 2016 and 2021. (b) Types of published papers about EEG.
Figure 4
Figure 4
The number of channels in EEG data is used to categorize features.
Figure 5
Figure 5
The process of EEG signal based on machine learning classifier.
See this image and copyright information in PMC

References

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