Over-aging and today’s lifestyle bring along an
increase in both prevalence and incidence of certain diseases such as dementia.
The majority of dementia cases is caused by Alzheimer's disease (AD), a
progressive fatal brain disorder that entails severe social and economic
At this time, there is no cure for AD and its cause and
progression are not completely understood. A definite diagnosis can only be
made post mortem. Clinical studies suggest biomedical (neural) signals as
Advanced digital biomedical signal processing are
necessary to identify biomarkers that improve early diagnostics and, as consequence,
clinical studies and the treatment of AD.
Main objective is
the study of neural and other Biomedical signals
of AD patients, and the application of
methods for automated feature extraction. These
features will be used for classification of
different stages of AD with the aim of providing
valuable diagnostic information for the Medical doctors.
In the project one
will analyze patterns in and the relation between biomedical signals such as
EEG, EOG and ECG, in order to identify healthy subjects and subjects with a
neurodegenerative disease. Since these biomedical signals are very sensitive
and prone to artefacts, careful automated preprocessing procedures have to
applied in close correspondence with the expertise of medical doctors. Patterns
in and the relation between the preprocessed features will then be quantified
and a set of features will be developed. Based on these features, an automated
classification of healthy subjects and subjects in different disease states
should be achieved with highest possible accuracy. The project is associated
with research groups from DTU and Rigshospitalet Glostrup.
In collaboration withProfessor, overlæge dr.med. Poul Jennum, Rigshospitalet, Glostrup
Signal processing, experience in Matlab and profound mathematical skills.