A Novel Real-time Depth of Anaesthesia Monitoring Method using Detrended Fluctuation Analysis and ANN

Chen, Xing and Wen, Paul ORCID: https://orcid.org/0000-0003-0939-9145 (2020) A Novel Real-time Depth of Anaesthesia Monitoring Method using Detrended Fluctuation Analysis and ANN. In: 2020 5th International Conference on Biomedical Signal and Image Processing (ICBIP 2020), 21-23 Aug 2020, Suzhou, China.


Abstract

In this paper, Detrended Fluctuation Analysis (DFA) method and artificial neural network (ANN) were applied to investigate the EEG variation during anesthesia. Rather than quantifying the loss of consciousness with a single feature, self-similarity, several cross-fluctuations from two channel raw EEG data using specific window sizes were extracted and combined to classify the patient anaesthesia state. The proposed method is evaluated using off-line data and the results are compared with the most widely used Bispectral (BIS) Index. In addition, the proposed method had reduced the calculation complexity for the real time implementation.


Statistics for USQ ePrint 42247
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: No
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -)
Date Deposited: 18 Jun 2021 04:54
Last Modified: 30 Jun 2021 22:20
Uncontrolled Keywords: Anaesthesia; artificial neural network; Detrended Fluctuation Analysis; EEG
Fields of Research (2008): 09 Engineering > 0903 Biomedical Engineering > 090302 Biomechanical Engineering
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified
40 ENGINEERING > 4003 Biomedical engineering > 400399 Biomedical engineering not elsewhere classified
Identification Number or DOI: https://doi.org/10.1145/3417519.3419403
URI: http://eprints.usq.edu.au/id/eprint/42247

Actions (login required)

View Item Archive Repository Staff Only