Intelligent Approaches for Alzheimer's Disease Diagnosis from EEG Signals: Systematic Review

Abstract

This systematic review explores the emerging field of Alzheimer's disease (AD) diagnosis using recent advances in machine learning (ML) and deep learning (DL) methods using EEG signals. This review focuses on 38 key articles published between January 2020 and February 2024, critically examining the integration of computational intelligence with neuroimaging to improve diagnostic accuracy and early detection of AD. AD poses significant diagnostic and treatment challenges, which are exacerbated by the aging of the global population. Traditional diagnostic methods, while comprehensive, are often limited by their time-consuming nature, reliance on expert interpretation, and limited accessibility. EEG is emerging as a promising alternative, providing a non-invasive, cost-effective way to record the brain's electrical activity and identify neurophysiological markers indicative of AD. The review highlights the shift towards automated diagnostic processes, where ML and DL techniques play a crucial role in analyzing EEG data, extracting relevant features and classifying AD stages with extremely high accuracy. It describes different methods for preprocessing EEG signals, feature extraction and application of different classifier models and demonstrates the complexity of the field and the nuanced understanding of EEG signals in the context of AD. In summary, although the review demonstrates several advantageous developments, it has highlighted critical challenges and limitations. For example, the AI needs more extensive and more diverse datasets to increase model generalizability and multi-modal data integration to achieve a more comprehensive AD diagnosis. Undoubtedly, its preprocessing techniques and classification techniques must be developed because of the complex nature of EEG data and AD pathology. To conclude, this review portrays EEG-based AD diagnosis as a promising field fueled by computational breakthroughs. Yet the insufficient literature and investigation require additional scientific inquiries and further research. Numerous outlooks highlight co-investigating EEG with complementary biomarkers and investigating innovative ML/DL approaches. Through the compilation of EEG prowess and computational cognition, the future appears bright for inclusive, precise, and early AD detection. Hence, the forthcoming possibilities of prompt intervention and individualized care are unfolding.