Electrical Electronics Engineering: Open Access
ISSN: 3071-0774 | DOI: 10.64030/3071-0774
Fathima K
As a relatively new, cost-effective, and non-invasive biomarker, electroencephalography (EEG) allows for the timely detection, classification, and differentiation of dementia-related conditions like Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). During the early stages of dementia, timely understanding of its prevalence can allow for effective therapies along with a proper understanding of EEG’s diagnostic capabilities. This systematic review focuses on existing studies that have utilized signal processing and machine learning with EEG data to classify, identify, and develop algorithms for different stages and types of dementia with an emphasis on MCI and AD. A systematic literature search was carried out on the Scopus, Web of Science, and PubMed databases for publications between 2010 and 2023. The search strategy was based on the following terms: EEG, Alzheimer, Mild Cognitive Impairment, Dementia, Brain Waves, Signal Processing, and Machine Learning. The selected peerreviewed articles had to have employed EEG techniques for classification and/or diagnosis. The review is guided by PRISMA for verification. criteria. Out of the 120 studies captured, we narrowed down to 57 studies that met all the inclusion criteria. The results show that EEG biomarkers, particularly spectral power, functional connectivity, and coherence, markedly differ among patients with MCI and AD. The application of multi-channel EEG diagnostics and feature extraction machine learning classifiers, such as SVM, CNN, and Random Forest, yield high accuracy levels sometimes exceeding 85%. EEG systems are particularly effective at detecting dementia in its early stages when combined with sophisticated computing models. That said, the variation in experimental workflows, including the number of participants and the techniques used to process signals, makes it difficult to compare the outcomes between different studies. Further research should aim at uniform designs alongside large sample testing.