Real-time depth of anaesthesia assessment using strong analytical signal transform technique
Article
Article Title | Real-time depth of anaesthesia assessment using strong analytical signal transform technique |
---|---|
ERA Journal ID | 5034 |
Article Category | Article |
Authors | Palendeng, Mario Elvis (Author), Wen, Peng (Author) and Li, Yan (Author) |
Journal Title | Physical and Engineering Sciences in Medicine |
Journal Citation | 37 (4), pp. 723-730 |
Number of Pages | 8 |
Year | 2014 |
Publisher | Springer |
Place of Publication | Netherlands |
ISSN | 0158-9938 |
1879-5447 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s13246-014-0309-2 |
Web Address (URL) | https://link.springer.com/article/10.1007/s13246-014-0309-2 |
Abstract | This paper introduces a new method addressing depth of anaesthesia (DoA) assessment for real-time monitoring. The new method uses a combination of phase and amplitude of electroencephalogram (EEG) signals to assess the DoA level. A strong analytical signal transform is applied to extract the phase and amplitude information of the recorded EEG signals. Based on the extracted features from the EEG signal in each different frequency band, a new DoA index is developed. The proposed new DoA index is evaluated using data from adult patients in an age range from 22 to 75 years. The results show that the new DoA index is able to detect the changing pattern of EEG signals early and agree with the clinical notes of an attending anaesthetist. The results are also closely correlated with the popular BIS index. Furthermore, the proposed new DoA index is able to detect the state changes earlier than the BIS index. |
Keywords | BIS; depth of anaesthesia; EEG signal; stationary wavelet transform; strong analytical signal |
ANZSRC Field of Research 2020 | 400607. Signal processing |
400305. Biomedical instrumentation | |
320201. Anaesthesiology | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
School of Mechanical and Electrical Engineering | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q2wxw/real-time-depth-of-anaesthesia-assessment-using-strong-analytical-signal-transform-technique
1712
total views7
total downloads3
views this month0
downloads this month