Methodological and Application Progress of EEG for Early Screening and Digital Intervention in Alzheimer's Disease
TL;DR
Electroencephalography has many benefits, including being non-invasive, cheap, and capable of repeated measurements, its general acceptance will, however, depend upon creating standard methods, validating using big samples, and integrating multimodal methods, including integrating EEG data with other imaging modalities and clinical evaluations to improve the accuracy of diagnosis.
Abstract
Alzheimer's disease (AD), which has a slow, insidious onset and minimal success in treatment, stresses the significance of early screening and intervention. The paper discusses the latest developments on how electroencephalography (EEG) can be used at an early stage to screen AD and digital intervention solutions. It provides an overview of some fundamental approaches like spectral analysis, functional connectivity analysis, and topographical analysis of a network of the brain. Moreover, it analyses the translational uses created by the application of those approaches in combination with multimodal features and machine learning, especially in the categorisation of risks and digital therapeutics. To summarise, EEG has many benefits, including being non-invasive, cheap, and capable of repeated measurements. Its general acceptance will, however, depend upon creating standard methods, validating using big samples, and integrating multimodal methods, including integrating EEG data with other imaging modalities and clinical evaluations to improve the accuracy of diagnosis. In the future, given that digital health care and intelligent analytics technology are still developing, it is expected that EEG will also be playing a more crucial part in the initial screening, intervention and long-term care of AD.