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-rodriguez
Edinburgh Research Explorer: http://www.research.ed.ac.uk/portal/jescuder