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

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• 作者：Tai Nguyen-Ky^a ; ^b ; ^{nguyentk@usq.edu.au}  ; [Author Vitae] ; Peng Wen^a ; ^b ; ^{pengwen@usq.edu.au}  ; [Author Vitae] ; Yan Li^a ; ^c ; ^{liyan@usq.edu.au}  ; [Author Vitae] ; Mel Malan^d ; ^{Mel_Malan@health.qld.gov.au}
• 关键词：Electroencephalogram (EEG) ; Wavelet transforms ; Eigenvector methods ; Daubechies wavelet ; Normalisation ; Depth of anaesthesia
• 刊名：Computers in Biology and Medicine
• 出版年：2012
• 期刊代码：70_00104825
• 类别：med
• 出版时间：June, 2012
• 卷：42
• 期：6
• 页码：680-691
• 文件大小：1190 K

摘要

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.