Measuring the hypnotic depth of anaesthesia based on the EEG signal using combined wavelet transform, eigenvector and normalisation techniques

Nguyen-Ky, Tai and Wen, Peng and Li, Yan and Malan, Mel (2012) Measuring the hypnotic depth of anaesthesia based on the EEG signal using combined wavelet transform, eigenvector and normalisation techniques. Computers in Biology and Medicine, 42 (6). pp. 680-691. ISSN 0010-4825

Abstract

This paper presents a new index to measure the hypnotic depth of anaesthesia (DoA) using EEG signals. This index is derived from applying combined Wavelet transform, eigenvector and normalisation techniques. The eigenvector method is first applied to build a feature function for six levels of coefficients in a discrete wavelet transform (DWT). The best Daubechies wavelet and their ranking value p are optimally determined to identify different states of anaesthesia. A statistic normalisation process is then carried out to re-scale data and compute the hypnotic depth of anaesthesia. Finally, a new function ZDoA is proposed to compute a DoA index which corresponds one of the five depths of anaesthesia states to very deep anaesthesia, deep anaesthesia, moderate anaesthesia, light anaesthesia and awake. Simulation results based on real anaesthetised EEGs demonstrate that the new index generally parallels the BIS index. In particular, the ZDoA index is often faster than the BIS index to react to the transition period between consciousness and unconsciousness for this data set. A Bland–Altman plot indicates a 95.23% agreement between the ZDoA and BIS indices. The ZDoA trend is responsive, and its movement is consistent with the clinically observed and recorded changes of the patients.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to published version due to publisher copyright policy (Elsevier)
Depositing User: Dr Tai Nguyen-Ky
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering
Date Deposited: 01 Oct 2012 08:08
Last Modified: 03 Jul 2013 01:12
Uncontrolled Keywords: electroencephalogram; EEG; wavelet transforms; eigenvector methods; daubechies wavelet; normalisation; depth of anaesthesia
Fields of Research (FOR2008): 09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
09 Engineering > 0903 Biomedical Engineering > 090302 Biomechanical Engineering
11 Medical and Health Sciences > 1103 Clinical Sciences > 110301 Anaesthesiology
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970111 Expanding Knowledge in the Medical and Health Sciences
Identification Number or DOI: doi: 10.1016/j.compbiomed.2012.03.004
URI: http://eprints.usq.edu.au/id/eprint/21344

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