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

Author:

Ouchani Mahshad1ORCID,Gharibzadeh Shahriar1ORCID,Jamshidi Mahdieh1ORCID,Amini Morteza23ORCID

Affiliation:

1. Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran

2. Shahid Beheshti University, Tehran, Iran

3. Institute for Cognitive Science Studies (ICSS), Tehran, Iran

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.

Publisher

Hindawi Limited

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine

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