DoA Assessment Based on EEG DFA and Entropy Features
Paper
Paper/Presentation Title | DoA Assessment Based on EEG DFA and Entropy Features |
---|---|
Presentation Type | Paper |
Authors | Chen, Xing, Song, Bo and Wen, Peng |
Journal or Proceedings Title | Brain Informatics |
Journal Citation | 15542, pp. 61-77 |
Number of Pages | 17 |
Year | 2025 |
Publisher | Springer |
Place of Publication | Singapore |
ISSN | 2198-4018 |
2198-4026 | |
ISBN | 9789819632961 |
9789819632978 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-96-3297-8_6 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-981-96-3297-8_6 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-981-96-3297-8 |
Conference/Event | Brain Informatics 2024 |
Event Details | Brain Informatics 2024 Delivery In person Event Location Bangkok Event Venue Thailand Event Description 17th International Conference, BI 2024, Bangkok, Thailand, December 13–15, 2024 Event Web Address (URL) |
Abstract | In this study, a novel method is proposed to combine modified detrended fluctuation analysis (MDFA) and entropy to extract features of electroencephalogram (EEG), which are then processed using a random forest algorithm to generate a new DoA index. The bispectral index (BIS) was used as the reference standard. The proposed DoA index achieved Pearson and Spearman correlation coefficients of 0.97 (p < 0.01) and 0.95 (p < 0.01) with the BIS index, respectively. Additionally, the mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) were 20.45, 4.52, and 2.85, respectively. These results indicate that the proposed DoA index is more accurate in patients’ consciousness level assessment. |
Keywords | Anesthesia; Depth of Anesthesia; Electroencephalogram |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | School of Engineering |
https://research.usq.edu.au/item/zyx33/doa-assessment-based-on-eeg-dfa-and-entropy-features
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