De-noising a raw EEG signal and measuring depth of anaesthesia for general anaesthesia patients

Nguyen-Ky, T. and Wen, Peng and Li, Yan and Gray, Robert (2010) De-noising a raw EEG signal and measuring depth of anaesthesia for general anaesthesia patients. In: 2010 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2010), 13-15 July 2010, Gold Coast, Australia.


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.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering
Date Deposited: 06 May 2011 02:22
Last Modified: 14 Mar 2018 02:54
Uncontrolled 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
Fields of Research : 10 Technology > 1004 Medical Biotechnology > 100402 Medical Biotechnology Diagnostics (incl. Biosensors)
09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified
11 Medical and Health Sciences > 1103 Clinical Sciences > 110301 Anaesthesiology
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: 10.1109/ICCME.2010.5558834

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