Measuring the hypnotic depth of anaesthesia based on the EEG signal using combined wavelet transform, eigenvector and normalisation techniques
Article
Article Title | Measuring the hypnotic depth of anaesthesia based on the EEG signal using combined wavelet transform, eigenvector and normalisation techniques |
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ERA Journal ID | 5040 |
Article Category | Article |
Authors | Nguyen-Ky, Tai (Author), Wen, Peng (Author), Li, Yan (Author) and Malan, Mel (Author) |
Journal Title | Computers in Biology and Medicine |
Journal Citation | 42 (6), pp. 680-691 |
Number of Pages | 12 |
Year | 2012 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0010-4825 |
1879-0534 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2012.03.004 |
Web Address (URL) | http://www.sciencedirect.com/science/article/pii/S0010482512000583 |
Abstract | This paper presents a new index to measure the hypnotic depth of anaesthesia (DoA) using EEG signals. This index is derived from applying combined Wavelet transform, eigenvector and normalisation techniques. The eigenvector method is first applied to build a feature function for six levels of coefficients in a discrete wavelet transform (DWT). The best Daubechies wavelet and their ranking value p are optimally determined to identify different states of anaesthesia. A statistic normalisation process is then carried out to re-scale data and compute the hypnotic depth of anaesthesia. Finally, a new function ZDoA is proposed to compute a DoA index which corresponds one of the five depths of anaesthesia states to very deep anaesthesia, deep anaesthesia, moderate anaesthesia, light anaesthesia and awake. Simulation results based on real anaesthetised EEGs demonstrate that the new index generally parallels the BIS index. In particular, the ZDoA index is often faster than the BIS index to react to the transition period between consciousness and unconsciousness for this data set. A Bland–Altman plot indicates a 95.23% agreement between the ZDoA and BIS indices. The ZDoA trend is responsive, and its movement is consistent with the clinically observed and recorded changes of the patients. |
Keywords | electroencephalogram; EEG; wavelet transforms; eigenvector methods; daubechies wavelet; normalisation; depth of anaesthesia |
ANZSRC Field of Research 2020 | 400607. Signal processing |
400303. Biomechanical engineering | |
320201. Anaesthesiology | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Centre for Systems Biology |
Department of Health, Queensland | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q151q/measuring-the-hypnotic-depth-of-anaesthesia-based-on-the-eeg-signal-using-combined-wavelet-transform-eigenvector-and-normalisation-techniques
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