Model-Based Estimation Methods for Improved Classification of Electroencephalographic Data: Application to Monitoring Depth of Anaesthesia

Grant number: LP120200773 | Funding period: 2013 - 2016

Completed

Abstract

This project will use advanced computing methods applied to electrical brain wave recordings to track how the brain changes as a person undergoes general anaesthesia during surgery. This will lead to better anaesthesia monitoring devices and reduce the chances of people experiencing pain during surgery or IQ deficits after the anaesthesia.

Related publications (3)

Assessing nitrous oxide effect using electroencephalographically-based depth of anesthesia measures cortical state and cortical input

L Kuhlmann, DTJ Liley

2018-02-01

Existing electroencephalography (EEG) based depth of anesthesia monitors cannot reliably track sedative or anesthetic states durin..

Tracking electroencephalographic changes using distributions of linear models: Application to propofol-based depth of anesthesia monitoring

L Kuhlmann, JH Manton, B Heyse, HEM Vereecke, T Lipping, MMRF Struys, DTJ Liley

2017-04-01

Objective: Tracking brain states with electrophysiological measurements often relies on short-term averages of extracted features ..

Neural mass model-based tracking of anesthetic brain states

L Kuhlmann, DR Freestone, JH Manton, B Heyse, HEM Vereecke, T Lipping, MMRF Struys, DTJ Liley

2016-01-01

Abstract Neural mass model-based tracking of brain states from electroencephalographic signals holds the promise of simultaneously..

University of Melbourne Researchers

Jonathan Manton