By Topic

Intelligent signal processing of evoked potentials for anaesthesia monitoring and control

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Elkfafi, M. ; Dept. of Autom. Control & Syst. Eng., Sheffield Univ., UK ; Shieh, J.S. ; Linkens, D.A. ; Peacock, J.E.

Depth of anaesthesia is hard to define and not readily measurable. Recently, attention has turned to evoked potentials (EPs) rather than the electroencephalogram (EEG) and they have been validated as a good measure of depth of anaesthesia. However, the amplitudes of the EPs vary from tenths of a microvolt to a few microvolts (μV), and are embedded in the spontaneous EEG waveform whose amplitude is typically 10 to 30 μV. An intelligent signal processing methodology for evoked potentials in anaesthesia monitoring and control is proposed in the paper. A model-based algorithm based upon autoregressive with exogenous input (ARX) models is used to improve the signal-to-noise ratio. Quantitative feature extraction is implemented to extract the factors describing the changes in amplitudes and latencies of the mid-latency auditory evoked response. In this way, three principal factors are obtained and then merged together using qualitative fuzzy logic to create a reliable index for monitoring depth of anaesthesia

Published in:

Control Theory and Applications, IEE Proceedings -  (Volume:144 ,  Issue: 4 )