FP6 BRACCIA

Description

The full name of the project is FP6 EU NEST BRACCIA.

• FP6 Framework Programme
• NEST New and Emerging Science and Technology Initiative
  
• BRACCIA (Brain, Respiration and Cardiac Causalities in Anaesthesia)
  

 

BRACCIA story from PUBLIC SERVICE REVIEW: European Union

 

Aim of the project

The aim of the BRACCIA project is to reduce the fraction of people who experience pain during surgical procedures carried out under general anaesthesia.  Within the project, researchers explore the coupled dynamics of the cardiorespiratory and neural systems of the human body.  The interactions and synchronization in the brain activity are studied using the ideas of the theory of complex networks.  Differences found in the dynamical states in awake and asleep patients will indicate possible ways of reliably measuring the depth of anaesthesia thereby improving the information the anaesthetist works with during a surgical procedure.  The research is divided into three main areas: data acquisition, modeling of the dynamics and data analysis.

The main project website is http://www.lancs.ac.uk/depts/physics/braccia/.

TEAM

The team of participants in the BRACCIA project is made of seven groups from diverse institutions across the European Union.

 

Task  of the NDW group

The Nonlinear Dynamics Workgroup is responsible for developing and applying robust algorithms which detect different dynamical states.  The development of such algorithms is an interdisciplinary endeavor bringing together physics, statistics and theoretical computer science. The design of the algorithms is based in the theory of dynamical systems, chaotic systems theory, stochastic dynamics and information theory. In practice, time-series analysis is difficult as generally the amount of available data and its quality is insufficient to allow the investigator to apply theoretical concepts directly. It is imperative that the designed algorithms and methods are tested on model systems which are constructed to mimic the dynamics (and noise properties) of real systems. Testing must be repeated many times so that statistically meaningful results are obtained. Because the tested models and analysis methods usually have several degrees of freedom, millions of iterations are needed to reliably estimate the quality of new algorithms. This step is facilitated by the use of computer clusters or supercomputers which provide answers that would take weeks or months to obtain on a standard desktop PC.