Background
Alzheimer's disease (AD) is a chronic, progressive and fatal brain disorder that entails severe social and economic consequences. AD is prevalent in the elder population and, with changes in demography and lifestyle, the number of AD cases is growing at an alarming rate. At this time, there is no cure for the disease and its cause and progression are not completely understood. The changes in the brain often start several years before a person experiences first symptoms such as memory problems. Diagnosing AD requires a multitude of clinical tests and does not yield absolute certainty.
Clinical studies suggest that an automated analysis of biomedical signals such as the electroencephalogram (EEG) and sleep patterns could improve AD diagnostics at an earlier disease stage and, thus, offer a possibility of adequate treatment and therapeutic planning. A large population could be screened with these non-invasive, widely available and non-expensive screenings.
Project Objectives
The aim of this project is to evaluate the capability of automatically computed markers from the EEG in AD diagnostics and to correlate these markers with the cognitive and physical changes that occur in the course of AD. For this purpose, data from the Danish Center for Healthy Aging and the Austrian Prospective Dementia Registry, both complying with the highest ethics standards, are used in this study that is conducted at the Technical University of Denmark (DTU). Careful data preparation and signal preprocessing provide the basis for a robust quantification of EEG and sleep changes in AD. This Danish-Austrian collaboration offers an opportunity for expanding the European AD-related research and, consequently, improving the diagnostics of AD.