Consciousness and depth of anesthesia assessment based on Bayesian analysis of EEG signals

Nguyen-Ky, Tai and Wen, Peng (Paul) and Li, Yan (2013) Consciousness and depth of anesthesia assessment based on Bayesian analysis of EEG signals. IEEE Transactions on Biomedical Engineering, 60 (6). pp. 1488-1498. ISSN 0018-9294

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Abstract

This study applies Bayesian techniques to analyse EEG signals for the assessment of the consciousness and depth of anaesthesia. This method takes the limiting large-sample normal distribution as posterior inferences to implement the Bayesian paradigm. The Maximum a Posterior (MAP) is applied to de-noise the wavelet coefficients based on a shrinkage function. When the anaesthesia states change from awake to light, moderate and deep anaesthesia, the MAP values increase gradually. Based on these changes, a new function BDoA is designed to assess the depth of anaesthesia. The new proposed method is evaluated using anaesthetized EEG recordings and BIS data from 25 patients. The Bland-Alman plot is used to verify the agreement of BDoA and the popular BIS index. Correlation between BDoA and BIS was measured using prediction probability (Pk). In order to estimate the accuracy of DoA, the effect of sample n and variance on the Maximum Posterior Probability (MPP) is studied. The results show that the new index accurately estimates the patient's hypnotic states. Compared with the BIS index in some cases, BDoA index can estimate the patient's hypnotic state in the case of poor signal quality.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version made not accessible.
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering
Date Deposited: 16 May 2013 01:47
Last Modified: 29 Sep 2014 01:26
Uncontrolled Keywords: electroencephalogram; EEG; wavelet transform; Bayesian; maximum A posterior; maximum posterior probability; depth of anaesthesia; DoA
Fields of Research : 10 Technology > 1004 Medical Biotechnology > 100402 Medical Biotechnology Diagnostics (incl. Biosensors)
09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified
11 Medical and Health Sciences > 1103 Clinical Sciences > 110301 Anaesthesiology
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: 10.1109/TBME.2012.2236649
URI: http://eprints.usq.edu.au/id/eprint/22653

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