Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods

Nguyen-Ky, Tai and Wen, Peng and Li, Yan (2014) Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods. IET Signal Processing, 8 (9). pp. 907-917. ISSN 1751-9675


This study proposes a novel index MLDoA to identify different anaesthetic states of a patient during surgery. Based on the new index MLDoA, the assessment of depth of anaesthesia (DoA) for a patient can be clearly monitored. Firstly, a modified Bayesian wavelet threshold is proposed to de-noise the electroencephalogram (EEG) signals. Secondly, the Hurst exponent is obtained to classify four states of anaesthesia: deep anaesthesia, moderate anaesthesia, light anaesthesia and awake. Finally, the index MLDoA is derived based on the Hurst exponent and maximum-likelihood function. The MLDoA index is evaluated
using clinically obtained EEG signals and the bispectral (BIS) data. The results show that the new index remains robust in the case of poor signal quality where BIS does not. Moreover, the new index MLDoA responds faster than the BIS index during the anaesthetic state transitions of patients. To validate the proposed method, the analysis of variance method is used to compare the new index MLDoA with the BIS index. The results indicate that the MLDoA distribution is better in distinguishing the five DoA states.

Statistics for USQ ePrint 26536
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to published version due to publisher copyright policy.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering
Date Deposited: 13 Jan 2015 06:25
Last Modified: 07 Jul 2016 04:57
Uncontrolled Keywords: anesthetics; Bayesian networks; electroencephalography
Fields of Research : 09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
01 Mathematical Sciences > 0102 Applied Mathematics > 010202 Biological Mathematics
11 Medical and Health Sciences > 1103 Clinical Sciences > 110301 Anaesthesiology
Socio-Economic Objective: C Society > 92 Health > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920118 Surgical Methods and Procedures
Identification Number or DOI: 10.1049/iet-spr.2013.0113

Actions (login required)

View Item Archive Repository Staff Only