Deep Neural EEG Based Alzeihmers Detection

Alzheimer disease (AD) is a progressive neurodegen- erative disorder, treatment and management of which needs to be timely. Conventional diagnosis techniques such as MRI, PET, and cerebrospinal fluid test are mostly invasive, costly, or not available at large scale. On the other hand, electroencephalography (EEG) represents a non-invasive, low-cost, and portable method of brain monitoring, hence, making it a favorable modality to use to screen AD at an early onset. The article contains a deep neural network framework of automatic EEG-based Alzheimer disease classification. Raw EEG data were processed and transformed to time-frequency representation and connection matrices. The different architectures (convolutional neural networks (CNNs), hybrid CNN-LSTM models as well as transformer-based models) were trained and compared with the conventional machine learning-based models (support vector machines and random forests). The deep neural models were found to be more effective than the traditional methods with improved accuracy, sensitiv- ity and specificity in distinguishing between the patients with AD and mild cognitive impairment (MCI) and those without mental illnesses. On top of highlighting frequency bands and areas that are correlated with known AD biomarkers, which is important to validate the clinical relevance of the finding, model interpretability methods, such as Grad-CAM, also focused on frequency bands and areas that are correlated with known AD biomarkers. These findings indicate that EEG-based deep neural models can be used practically to scalably and early diagnose the presence of Alzheimer as not only deep learning using EEG signals improves diagnostic accuracy but also provides biologically relevant outcomes.