Robust approach for depth of anaesthesia assessment based on hybrid transform and statistical features

Diykh, Mohammed and Miften, Firas Sabar and Abdulla, Shahab ORCID: and Saleh, Khalid and Green, Jonathan H. ORCID: (2020) Robust approach for depth of anaesthesia assessment based on hybrid transform and statistical features. IET Science, Measurement & Technology, 14 (1). pp. 128-136. ISSN 1751-8822

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A Robust Approach to Depth of Anaesthesia Assessment Based on Hybrid Transform and Statistical Feature-Shahab.pdf

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To develop an accurate and efficient depth of anaesthesia (DoA) assessment technique that could help anaesthesiologists to trace the patient’s anaesthetic state during surgery, a new automated DoA approach was proposed. It applied Wavelet-Fourier analysis (WFA) to extract the statistical characteristics from an anaesthetic EEG signal and to designed a new DoA index. In this proposed method, firstly, the wavelet transform was applied to a denoised EEG signal, and a Fast Fourier transform was then applied to the wavelet detail coefficient D3. Ten statistical features were extracted and analysed, and from these, five features were selected for designing a new index for the DoA assessment. Finally, a new DoA (WFADoA) was developed and compared with the most popular bispectral index (BIS) monitor. The results from the testing set showed that there were very high correlations between the WFADoA and the BIS index during the awake, light and deep anaesthetic stages. In the case of poor signal quality, the BIS index and the WFADoA were also tested, and the obtained results demonstrated that the WFADoA could indicate the DoA values, while the BIS failed to show valid outputs for those situations.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2019 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.
Faculty/School / Institute/Centre: Historic - Open Access College (1 Jul 2013 - 7 Jun 2020)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - No Department (1 Jul 2013 -)
Date Deposited: 28 Jan 2020 05:12
Last Modified: 25 Aug 2020 01:26
Uncontrolled Keywords: electroencephalography; fast Fourier transforms; medical signal processing; patient monitoring; wavelet transforms; neurophysiology; surgery; direction-of-arrival estimation; signal denoising; statistical analysis; feature selection; feature extraction; statistical feature extraction; DoA assessment; bispectral index monitor; BIS index; DoA values; efficient depth; anaesthesia assessment technique; automated DoA approach; wavelet-Fourier analysis; anaesthetic electroencephalogram signal; DoA index; wavelet transform; fast Fourier transform; robust approach; hybrid transform; patient anaesthetic state; surgery; EEG; denoised EEG signal; feature selection
Fields of Research (2008): 06 Biological Sciences > 0699 Other Biological Sciences > 069999 Biological Sciences not elsewhere classified
Fields of Research (2020): 31 BIOLOGICAL SCIENCES > 3199 Other biological sciences > 319999 Other biological sciences not elsewhere classified
Socio-Economic Objectives (2008): C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
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