AI-Driven Framework for Accurate Detection of Alzheimer’s Disease in EEG

Alzheimer’s Disease (AD) is a rapidly growing neurodegenerative disorder that severely impairs cognitive function, particularly among older adults. Early detection is critical for timely intervention and effective management. While electroencephalography (EEG) provides a non-invasive, cost-effective tool with high temporal resolution for diagnosing Alzheimer’s Disease (AD), traditional EEG-based approaches often struggle to extract informative features accurately from complex brain signals, thus limiting their diagnostic performance. This study addresses these challenges by proposing a novel artificial intelligence-based framework that integrates feature fusion with a Convolutional Long Short-Term Memory (Conv-LSTM) architecture.