Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence

Abstract

This comprehensive review examines the integration of Quantitative Electroencephalography (qEEG) and Artificial Intelligence (AI) in the detection and diagnosis of Alzheimer's Disease (AD). Through systematic analysis of 11 key studies across multiple international databases, we evaluated various AI architectures, including machine learning algorithms and deep learning networks, applied to qEEG data for AD detection. The review encompasses studies with diverse subject populations, ranging from 35 to 890 participants, with mean ages between 66.94 and 74.8 years. Results demonstrate that AI-enhanced qEEG analysis achieves remarkable diagnostic accuracy, with Linear Discriminant Analysis (LDA) reaching 93.18% accuracy and 97.92% Area Under Curve (AUC). Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) also showed promising results, with some models achieving up to 100% sensitivity in specific classifications. The integration of multiple data types and advanced feature extraction methods significantly improved diagnostic precision. Geographic diversity in research origins, spanning from Asia to Europe and the Americas, provides robust cross-cultural validation of findings. However, challenges persist in data quality, computational resources, and standardization of methodologies. This review highlights the significant potential of AI-enhanced qEEG as a non-invasive, cost-effective tool for the diagnosis of AD in its prodromal and dementia stages, while also identifying areas requiring further research to optimize its clinical application. These findings suggest that AI-enhanced qEEG analysis could revolutionize AD diagnosis, enabling earlier intervention and improved patient outcomes.

Keywords: Alzheimer's disease; Artificial intelligence; Early detection; Machine learning; Quantitative electroencephalography.