Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods
Article
Article Title | Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods |
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ERA Journal ID | 41674 |
Article Category | Article |
Authors | Nguyen-Ky, Tai (Author), Wen, Peng (Author) and Li, Yan (Author) |
Journal Title | IET Signal Processing |
Journal Citation | 8 (9), pp. 907-917 |
Number of Pages | 11 |
Year | 2014 |
Place of Publication | United Kingdom |
ISSN | 1751-9675 |
Digital Object Identifier (DOI) | https://doi.org/10.1049/iet-spr.2013.0113 |
Web Address (URL) | https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/iet-spr.2013.0113 |
Abstract | 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. |
Keywords | anesthetics; Bayesian networks; electroencephalography |
ANZSRC Field of Research 2020 | 400607. Signal processing |
490102. Biological mathematics | |
320201. Anaesthesiology | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Mechanical and Electrical Engineering |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q2w57/monitoring-the-depth-of-anaesthesia-using-hurst-exponent-and-bayesian-methods
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