The University of Southampton
University of Southampton Institutional Repository

Comparison of correlation dimension and fractal dimension in estimating level of consciousness

Comparison of correlation dimension and fractal dimension in estimating level of consciousness
Comparison of correlation dimension and fractal dimension in estimating level of consciousness
This paper compares the correlation dimension (D2) ,Higuchi fractal dimension (HFD),Katz fractal dimension(KFD)and Sevcik fractal dimension(SFD) approaches in estimating Depth of Anesthesia (DOA) based on of electroencephalogram (EEG). The single-channel EEG data was captured in both ICU and operating room and different anesthetic drugs, including propofol and isoflurane were used. For better analysis, application of adaptive segmentation on EEG signal for estimating DOA is evaluated and compared to fixed segmentation. Prediction probability (PK) is used as a measure of correlation between the predictors and BIS index to evaluate the proposed methods. The results show the ability of these algorithms in predicting DOA. Also, evolving fixed and adaptive windowing methods for segmentation of EEG reveals no meaningful difference in estimate DOA.
Zaghari, Bahareh
a0537db6-0dce-49a2-8103-0f4599ab5f6a
Zaghari, Bahareh
a0537db6-0dce-49a2-8103-0f4599ab5f6a

Zaghari, Bahareh (2009) Comparison of correlation dimension and fractal dimension in estimating level of consciousness. Conference on Biomedical Engineering, Mashhad, Iran, Islamic Republic of. 01 Oct 2009.

Record type: Conference or Workshop Item (Other)

Abstract

This paper compares the correlation dimension (D2) ,Higuchi fractal dimension (HFD),Katz fractal dimension(KFD)and Sevcik fractal dimension(SFD) approaches in estimating Depth of Anesthesia (DOA) based on of electroencephalogram (EEG). The single-channel EEG data was captured in both ICU and operating room and different anesthetic drugs, including propofol and isoflurane were used. For better analysis, application of adaptive segmentation on EEG signal for estimating DOA is evaluated and compared to fixed segmentation. Prediction probability (PK) is used as a measure of correlation between the predictors and BIS index to evaluate the proposed methods. The results show the ability of these algorithms in predicting DOA. Also, evolving fixed and adaptive windowing methods for segmentation of EEG reveals no meaningful difference in estimate DOA.

Text
__soton.ac.uk_ude_personalfiles_users_bz2e11_mydesktop_My publication_Mashhad_CONF_paper.pdf - Author's Original
Download (358kB)

More information

Published date: 8 October 2009
Venue - Dates: Conference on Biomedical Engineering, Mashhad, Iran, Islamic Republic of, 2009-10-01 - 2009-10-01
Organisations: Dynamics Group

Identifiers

Local EPrints ID: 352997
URI: http://eprints.soton.ac.uk/id/eprint/352997
PURE UUID: ac5a3fba-201a-48d6-b16a-a31533fdf89f

Catalogue record

Date deposited: 28 May 2013 13:27
Last modified: 14 Mar 2024 13:59

Export record

Contributors

Author: Bahareh Zaghari

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×