De-noising a raw EEG signal and measuring depth of anaesthesia for general anaesthesia patients
Paper
Paper/Presentation Title | De-noising a raw EEG signal and measuring depth of anaesthesia for general anaesthesia patients |
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Presentation Type | Paper |
Authors | Nguyen-Ky, T. (Author), Wen, Peng (Author), Li, Yan (Author) and Gray, Robert (Author) |
Editors | Li, Yan, Yang, Jiajia, Wen, Peng and Wu, Jinglong |
Journal or Proceedings Title | Proceedings of the 2010 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2010) |
Number of Pages | 6 |
Year | 2010 |
Place of Publication | Brisbane, Australia |
ISBN | 9781424468430 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICCME.2010.5558834 |
Web Address (URL) of Paper | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5558834 |
Conference/Event | 2010 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2010) |
Event Details | 2010 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2010) Parent ICME International Conference on Complex Medical Engineering Event Date 13 to end of 15 Jul 2010 Event Location Gold Coast, Australia |
Abstract | In monitoring the depth of anaesthesia, raw EEG signals are recorded by means of an adhesive sensor attached to the forehead. The raw EEG signal is often corrupted by spike, low frequency and high frequency noise. Removal of such noise improves clinical utility and this paper presents a novel method which uses a double wavelet-based de-noising algorithm. The results of experimental simulations show that the proposed method reproduces the EEG signal almost noiselessly. The resultant data is suitable input for monitoring the depth of anaesthesia. We propose to build up a wavelet-based Depth of Anaesthesia (WDoA) based on discrete wavelet transform (DWT) and power spectral density (PSD) function. Findings give very close correlation between the WDoA and BIS Index values, through the whole scale from 100 to 0 with full recording time on patient. Simulation results demonstrate that this new index, WDoA, represents the DoA in all anaesthesia states reliably and accurately. |
Keywords | anaesthesia; EEG; noise; adhesive sensor; anaesthesia depth measurement; anaesthesia wavelet-based depth; bispectral index; double wavelet-based de-noising algorithm; general anaesthesia patients; high frequency noise; low frequency noise; patient monitoring; power spectral density function; raw EEG signal |
ANZSRC Field of Research 2020 | 320602. Medical biotechnology diagnostics (incl. biosensors) |
400399. Biomedical engineering not elsewhere classified | |
320201. Anaesthesiology | |
Public Notes | © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | Department of Electrical, Electronic and Computer Engineering |
Centre for Systems Biology | |
Department of Health, Queensland | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q0749/de-noising-a-raw-eeg-signal-and-measuring-depth-of-anaesthesia-for-general-anaesthesia-patients
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