Real-Time Depth of Anaesthesia Assessment Based on Hybrid Statistical Features of EEG
Article
Article Title | Real-Time Depth of Anaesthesia Assessment Based on Hybrid Statistical Features of EEG |
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ERA Journal ID | 34304 |
Article Category | Article |
Authors | Huang, Yi (Author), Wen, Peng (Author), Song, Bo (Author) and Li, Yan (Author) |
Journal Title | Sensors |
Journal Citation | 22 (16) |
Article Number | 6099 |
Number of Pages | 15 |
Year | Aug 2022 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1424-8220 |
1424-8239 | |
Digital Object Identifier (DOI) | https://doi.org/10.3390/s22166099 |
Web Address (URL) | https://www.mdpi.com/1424-8220/22/16/6099 |
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. |
Keywords | EEG; depth of anaesthesia; real-time; machine learning |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
460207. Modelling and simulation | |
460102. Applications in health | |
461199. Machine learning not elsewhere classified | |
Byline Affiliations | School of Engineering |
School of Mathematics, Physics and Computing | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7q58/real-time-depth-of-anaesthesia-assessment-based-on-hybrid-statistical-features-of-eeg
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