Complex networks approach for depth of anesthesia assessment

Diykh, Mohammed and Li, Yan and Wen, Peng and Li, Tianning (2018) Complex networks approach for depth of anesthesia assessment. Measurement, 119. pp. 178-189. ISSN 0263-2241


Abstract

Despite numerous attempts to develop a reliable depth of anesthesia (DoA) index to avoid patients’ intraoperative awareness during surgery, designing an accurate DoA index is a grand challenge in anesthesia research. In this paper, an attempt is made to design a new DoA index. We applied a statistical model and spectral graph wavelet transform (SGWT) to monitor the DoA. The de-noised electroencephalography (EEG) signals are partitioned into segments using a window technique. The window size is determined empirically, then each EEG segment is divided into sub-blocks to make the signal quasi stationary. 10 statistical characteristics are extracted from each sub-block. As a result, a vector of statistical characteristics is pulled out from each segment. Each vector of the features is then mapped as a weighted graph and spectral graph wavelet transform is performed. The total energy of wavelet coefficients at different scales is tested. The energy of wavelet coefficients at scale 3 is selected to form a SGWTDoA function. The SGWTDoA is evaluated using an anesthesia EEG recordings and the bispectral (BIS) from 22 subjects. The Bland-Altman, regression, Q-Q plot and Pearson correlation are used to verify the agreement between the SGWTDoA and the BIS. The experimental results demonstrate that the SGWTDoA has the ability to estimate the DoA accurately. The SGWTDoA is also compared and tested with the BIS in the case of poor signal quality. Our findings show that, the SGWTDoA can reflect the transition from unconsciousness to consciousness efficiently even for a poor signal while the BIS fails to display the DoA values on the monitor.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Date Deposited: 29 Apr 2020 05:17
Last Modified: 07 May 2020 03:33
Uncontrolled Keywords: depth of anesthesia; statistical model; spectral graph wavelet transform; poor signal quality
Fields of Research (2008): 08 Information and Computing Sciences > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
40 ENGINEERING > 4006 Communications engineering > 400607 Signal processing
Identification Number or DOI: https://doi.org/10.1016/j.measurement.2018.01.024
URI: http://eprints.usq.edu.au/id/eprint/38113

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