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.
Highlights
Alzheimer’s disease (AD) is a neurological disease and is the most common form of age-related dementia in today’s culture
The thesis is aimed at investigating robust functional biomarkers dependent on time-frequency features of Quantitative EEG (qEEG) and developing a computer-aided discriminant scheme for automatically classification EEG signals of AD and normal elderly controls (NC) as a result of the promising results obtained with the wavelet transform analysis and machine learning methods [13]
These findings indicate that the frontal, temporal, and central EEG impulses in AD and mild cognitive impairment (MCI) patients’ brains were slightly less complex than those in healthy controls (HC)
Summary
Alzheimer’s disease (AD) is a neurological disease and is the most common form of age-related dementia in today’s culture. QEEG offers objective and quantifiable data that can be replicated in subsequent trials, as well as the benefits of having less laboratory protocols and lower costs [8] This makes it ideal for screening large-scale and early detection of AD. The thesis is aimed at investigating robust functional biomarkers dependent on time-frequency features of qEEG and developing a computer-aided discriminant scheme for automatically classification EEG signals of AD and normal elderly controls (NC) as a result of the promising results obtained with the wavelet transform analysis and machine learning methods [13]. Many experiments have looked at multiscale entropy and MSE-based measurements of EEG signals from Alzheimer’s patients. A total of 123 EEG recordings were obtained from stable people, people with very minor AD, people with mild AD, and people with moderate to serious AD
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