Real time depth of anaesthesia monitoring through electroencephalogram (EEG) signal analysis based on Bayesian method and analytical technique

PhD Thesis


Palendeng, Mario Elvis. 2015. Real time depth of anaesthesia monitoring through electroencephalogram (EEG) signal analysis based on Bayesian method and analytical technique. PhD Thesis Doctor of Philosophy. University of Southern Queensland.
Title

Real time depth of anaesthesia monitoring through electroencephalogram (EEG) signal analysis based on Bayesian method and analytical technique

TypePhD Thesis
Authors
AuthorPalendeng, Mario Elvis
SupervisorWen, Peng (Paul)
Li, Yan
Institution of OriginUniversity of Southern Queensland
Qualification NameDoctor of Philosophy
Number of Pages167
Year2015
Abstract

The electroencephalogram (EEG) signal from the brain is used for analysing brain abnormality, diseases, and monitoring patient conditions during surgery. One of the applications of the EEG signals analysis is real-time anaesthesia monitoring, as the anaesthetic drugs normally targeted the central nervous system.

Depth of anaesthesia has been clinically assessed through breathing pattern, heart rate, arterial blood pressure, pupil dilation, sweating and the presence of movement. Those assessments are useful but are an indirect-measurement of anaesthetic drug effects. A direct method of assessment is through EEG signals because most anaesthetic drugs affect neuronal activity and cause a changed pattern in EEG signals.

The aim of this research is to improve real-time anaesthesia assessment through EEG signal analysis which includes the filtering process, EEG features extraction and signal analysis for depth of anaesthesia assessment. The first phase of the research is EEG signal acquisition. When EEG signal is recorded, noises are also recorded along with the brain waves. Therefore, the filtering is necessary for EEG signal analysis.

The filtering method introduced in this dissertation is Bayesian adaptive least mean square (LMS) filter which applies the Bayesian based method to find the best filter weight step for filter adaptation. The results show that the filtering technique is able to remove the unwanted signals from the EEG signals.

This dissertation proposed three methods for EEG signal features extraction and analysing. The first is the strong analytical signal analysis which is based on the Hilbert transform for EEG signal features' extraction and analysis. The second is to extract EEG signal features using the Bayesian spike accumulation technique. The third is to apply the robust Bayesian Student-t distribution for real-time anaesthesia assessment.

Computational results from the three methods are analysed and compared with the recorded BIS index which is the most popular and widely accepted depth of anaesthesia monitor. The outcomes show that computation times from the three methods are leading the BIS index approximately 18-120 seconds. Furthermore, the responses to anaesthetic drugs are verified with the anaesthetist's documentation and then compared with the BIS index to evaluate the performance. The results indicate that the three methods are able to extract EEG signal features efficiently, improve computation time, and respond faster to anaesthetic drugs compared to the existing BIS index.

Keywordselectroencephalogram signal analysis; EEG signal analysis; anaesthesia monitoring; depth of anaesthesia
ANZSRC Field of Research 2020400399. Biomedical engineering not elsewhere classified
400305. Biomedical instrumentation
Byline AffiliationsSchool of Mechanical and Electrical Engineering
Permalink -

https://research.usq.edu.au/item/q4v05/real-time-depth-of-anaesthesia-monitoring-through-electroencephalogram-eeg-signal-analysis-based-on-bayesian-method-and-analytical-technique

Download files


Published Version
Palendeng_2015_whole.pdf
File access level: Anyone

  • 577
    total views
  • 188
    total downloads
  • 2
    views this month
  • 1
    downloads this month

Export as

Related outputs

Real-time depth of anaesthesia assessment using strong analytical signal transform technique
Palendeng, Mario Elvis, Wen, Peng and Li, Yan. 2014. "Real-time depth of anaesthesia assessment using strong analytical signal transform technique." Physical and Engineering Sciences in Medicine. 37 (4), pp. 723-730. https://doi.org/10.1007/s13246-014-0309-2
EEG data compression to monitor DoA in telemedicine
Palendeng, Mario E., Zhang, Qing, Pang, Chaoyi and Li, Yan. 2012. "EEG data compression to monitor DoA in telemedicine." Studies in Health Technology and Informatics. 178, pp. 163-8. https://doi.org/10.3233/978-1-61499-078-9-163
Investigation of bispectral index filtering and improvement using wavelet transform adaptive filter
Palendeng, Mario Elvis, Wen, Peng and Goh, Steven. 2010. "Investigation of bispectral index filtering and improvement using wavelet transform adaptive filter." Kuo, Way, Tsui, Lap Chee and Sung, Jao Yiu (ed.) 4th IEEE International Conference on Nano/Molecular Medicine and Engineering (NANOMED 2010). Hong Kong, China 05 - 09 Dec 2010 Piscataway, NJ. United States. https://doi.org/10.1109/NANOMED.2010.5749796
Removing noise from electroencephalogram signals for BIS based depth of anaesthesia monitors
Palendeng, Mario Elvis. 2011. Removing noise from electroencephalogram signals for BIS based depth of anaesthesia monitors. Masters Thesis Master of Engineering (Research). University of Southern Queensland.