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CONSCIOUSNESS AND DEPTH OF ANAESTHESIA ASSESSMENT BASED ON BAYESIAN ANALYSIS OF EEG SIGNALS.
MedLine Citation:
PMID:  23314762     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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 ä 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.
Authors:
T Nguyen-Ky; P Wen; Y Li
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-09
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  -     ISSN:  1558-2531     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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