Machine Learning to Predict Cognitive Decline of Patients with Alzheimer's Disease Using EEG Markers: A Preliminary Study

Alzheimer's disease causes most of dementia cases. Although currently there is no cure for this disease, predicting the cognitive decline of people at the first stage of the disease allows clinicians to alleviate its burden. Clinicians evaluate individuals' cognitive decline by using neuropsychological tests consisting of different sections, each devoted to testing a specific set of cognitive skills. In this paper, we present the results of a preliminary study aimed at assessing the ability of machine learning based tools to predict the cognitive decline of Alzheimer's patients using features extracted from EEG records at resting state. We tested seven classification schemes in predicting nine scores, provided by different sections of four neuropsychological tests. The experimental results demonstrated that at least three of these scores allows EEG-based features to be effective in predicting the cognitive decline of Alzheimer's patients by using machine learning tools.