Depth of anaesthesia monitors and the latest algorithms
Article
Article Title | Depth of anaesthesia monitors and the latest algorithms |
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ERA Journal ID | 210161 |
Article Category | Article |
Authors | Li, Tianning (Author) and Li, Yan (Author) |
Journal Title | Asian Pacific Journal of Tropical Medicine |
Journal Citation | 7 (6), pp. 429-437 |
Number of Pages | 9 |
Year | Jun 2014 |
Place of Publication | Singapore |
ISSN | 1995-7645 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/S1995-7645(14)60070-5 |
Web Address (URL) | http://www.sciencedirect.com/science/article/pii/S1995764514600705 |
Abstract | This paper reviews the existing depth of anaesthesia (DoA) monitors and their algorithms, and proposes to improve their performance from four aspects. An ideal DoA monitor should be able to suggest a personalised drug dosages, to predict and provide early warnings when dosages are inappropriate, to be portable and highly cost-effective. The limitations of the existing DoA monitors commonly include unsatisfied data filtering techniques, time delay for the monitoring responses, inflexible and low noise immunity problems. The latest research results show that their performance can be improved using up-to-date computing technology and neurophysiology. The findings in Chinese market review shows that neither the imported nor the Chinese domestic DoA monitors are widely utilised at hospitals, but the demand for DoA monitors is very high. Clearly there is a high demand which encourages the development of a better DoA monitor and its mass production in China. |
Keywords | Depth of anaesthesia; Consciousness; Electroencephalograph |
Contains Sensitive Content | Does not contain sensitive content |
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 | Faculty of Health, Engineering and Sciences |
School of Mechanical and Electrical Engineering | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q2753/depth-of-anaesthesia-monitors-and-the-latest-algorithms
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