Real-Time Depth of Anaesthesia Assessment Based on Hybrid Statistical Features of EEG

Huang, Yi and Wen, Peng ORCID: https://orcid.org/0000-0003-0939-9145 and Song, Bo and Li, Yan ORCID: https://orcid.org/0000-0002-4694-4926 (2022) Real-Time Depth of Anaesthesia Assessment Based on Hybrid Statistical Features of EEG. Sensors, 22 (16):6099. pp. 1-15.

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Abstract

This paper proposed a new depth of anaesthesia (DoA) index for the real-time assessment of DoA using electroencephalography (EEG). In the proposed new DoA index, a wavelet transform threshold was applied to denoise raw EEG signals, and five features were extracted to construct classification models. Then, the Gaussian process regression model was employed for real-time assessment of anaesthesia states. The proposed real-time DoA index was implemented using a sliding window technique and validated using clinical EEG data recorded with the most popular commercial DoA product Bispectral Index monitor (BIS). The results are evaluated using the correlation coefficients and Bland–Altman methods. The outcomes show that the highest and the average correlation coefficients are 0.840 and 0.814, respectively, in the testing dataset. Meanwhile, the scatter plot of Bland–Altman shows that the agreement between BIS and the proposed index is 94.91%. In contrast, the proposed index is free from the electromyography (EMG) effect and surpasses the BIS performance when the signal quality indicator (SQI) is lower than 15, as the proposed index can display high correlation and reliable assessment results compared with clinic observations.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Engineering (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Date Deposited: 17 Aug 2022 00:06
Last Modified: 28 Sep 2022 04:32
Uncontrolled Keywords: EEG; depth of anaesthesia; real-time; machine learning
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460102 Applications in health
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified
Identification Number or DOI: https://doi.org/10.3390/s22166099
URI: http://eprints.usq.edu.au/id/eprint/50948

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