In this video Javier describes his research in the processing of
biomedical time series to tackle clinical problems; particularly
understanding how the brain activity changes with ageing and
neurodegenerative and psychiatric diseases.
Background:
Arguably, the brain is the most complex organ of the human body. It
enables us to detect and react to changes in the environment and it is
also responsible for our cognitive functions. Electrophysiological
techniques such as the electroencephalogram (EEG) and the
magnetoencephalogram (MEG) provide an unparalleled opportunity to study
the brain activity non-invasively and with high temporal resolution.
These two techniques record the electromagnetic fields created by the
neural activity directly.
The EEG and MEG activity has been analysed to monitor and
characterise diverse brain diseases and mental states. Some of the
conditions that affect the brain lead to huge societal and economical
costs. For instance, Alzheimer’s disease (AD) is the most common
neurodegenerative disease in older people. In 2010, there were 35.6
million patients with dementia worldwide and this number will almost
double every 20 years. This and other brain disorders have a prodromal
phase in which the brain is suffering pathological attacks but the
clinical symptoms have not appeared yet.
Therefore, there is a need for objective means to help clinicians in
the early detection and monitoring of brain diseases. It would also be
extremely beneficial to understand how the brain activity matures with
age to try to detect patterns of activity that deviate from normal
health. The processing of the brain signals can contribute to these
tasks by creating ways to enhance the quality of the recordings and by
providing mathematical tools to extract relevant information from the
signals.
Biomedical signal processing plays an essential role in data
preprocessing to improve the quality of the recordings. It allows us to
reduce the level of certain noises and artefacts appearing in the
physiological time series and improve the reliability of further
analyses.
Such analyses may include the computation of many different types of
features depending on the problem at hand. Multivariate signal
processing techniques are particularly relevant when processing brain
activity as they allow us to account for the segregation and integration
of information that happens in the brain. In order to do so, recent
developments in brain connectivity and graph theory allow us to analyse
and understand brain activity from a system’s perspective.
Find out more:
Dr Javier Escudero, School of Engineering profile:
http://www.eng.ed.ac.uk/about/people/dr-javier-escudero-rodriguezEdinburgh Research Explorer:
http://www.research.ed.ac.uk/portal/jescuder
