Extraction and interpretation of EEG features for diagnosis and severity prediction of Alzheimer’s Disease and Frontotemporal dementia using deep learning

Alzheimer’s Disease (AD) is the most common form of dementia, characterized by progressive cognitive decline and memory loss. Frontotemporal dementia (FTD), the second most common form of dementia, affects the frontal and temporal lobes, causing changes in personality, behavior, and language. Due to overlapping symptoms, FTD is often misdiagnosed as AD. Although electroencephalography (EEG) is portable, non-invasive, and cost-effective, its diagnostic potential for AD and FTD is limited by the similarities between the two diseases. To address this, we introduce an EEG-based feature extraction method to identify and predict the severity of AD and FTD using deep learning. Key findings include increased delta band activities in the frontal and central regions as biomarkers. By extracting temporal and spectral features from EEG signals, our model combines a Convolutional Neural Network with an attention-based Long Short-Term Memory (aLSTM) network, achieving over 90% accuracy in distinguishing AD and FTD from cognitively normal (CN) individuals. It also predicts severity with relative errors of less than 35% for AD and approximately 15.5% for FTD. Differentiating FTD from AD remains challenging due to shared characteristics. However, applying a feature selection procedure improves the specificity in separating AD from FTD, increasing it from 26% to 65%. Building on this, we developed a two-stage approach to classify AD, CN, and FTD simultaneously. In this approach, CN is identified first, followed by the differentiation of FTD from AD. This method achieves an overall accuracy of 84% in classifying AD, CN, and FTD.