Robust approach for depth of anaesthesia assessment based on hybrid transform and statistical features
Article
Article Title | Robust approach for depth of anaesthesia assessment based on hybrid transform and statistical features |
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ERA Journal ID | 4421 |
Article Category | Article |
Authors | Diykh, Mohammed (Author), Miften, Firas Sabar (Author), Abdulla, Shahab (Author), Saleh, Khalid (Author) and Green, Jonathan H. (Author) |
Journal Title | IET Science, Measurement and Technology |
Journal Citation | 14 (1), pp. 128-136 |
Number of Pages | 9 |
Year | 2020 |
Publisher | Institution of Engineering and Technology (IET) |
Place of Publication | United Kingdom |
ISSN | 1751-8822 |
1751-8830 | |
Digital Object Identifier (DOI) | https://doi.org/10.1049/iet-smt.2018.5393 |
Web Address (URL) | https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-smt.2018.5393 |
Abstract | 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. |
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 |
ANZSRC Field of Research 2020 | 319999. Other biological sciences not elsewhere classified |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
University of Thi-Qar, Iraq | |
Open Access College | |
School of Mechanical and Electrical Engineering | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q575z/robust-approach-for-depth-of-anaesthesia-assessment-based-on-hybrid-transform-and-statistical-features
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