Advancing Alzheimer's disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects memory, thinking, and behavior, leading to dementia, a severe cognitive decline. While no cure currently exists, recent advancements in preventive drug trials and therapeutic management have increased interest in developing clinical algorithms for early detection and biomarker identification. Electroencephalography (EEG) is noninvasive, cost-effective, and has high temporal resolution, making it a promising tool for automated AD detection. However, conventional machine learning approaches often fall short in accurately detecting AD due to their limited architectures. We also need to investigate the impact of EEG signal segment length on classification accuracy. To address these issues, a deep learning-based framework is proposed to detect AD using EEG data, focusing on determining the optimal segment length for classification. This framework contains EEG data collection, pre-processing for noise removal, temporal segmentation, convolutional neural network (CNN) model training and classification, and finally, evaluation. We have tested different segment lengths to test the impact on AD detection. We have used both 10-fold and leave-one-out cross-validation techniques and obtained accuracy of 97.08% and 96.90%, respectively, on a publicly available dataset from AHEPA General University Hospital of Thessaloniki. We have also tested the generalizability of the proposed model by testing it to detect frontotemporal dementia and obtained better results than existing studies. Furthermore, we have validated our proposed CNN model using several ablation studies and layer-wise extracted feature visualization. This study will establish a pioneering direction for future researchers and technology experts in the field of neurodiseases.