Brunel University Research Archive(BURA) preserves and enables easy and open access to all
types of digital content. It showcases Brunel's research outputs.
Research contained within BURA is open access, although some publications may be subject
to publisher imposed embargoes. All awarded PhD theses are also archived on BURA.
Browsing by Subject Depth of anesthesia
Showing results 1 to 5 of 5
| Issue Date | Title | Author(s) |
|---|---|---|
| 2012 | Adaptive computation of multiscale entropy and its application in EEG signals for monitoring depth of anesthesia during surgery | Liu, Q; Wei, Q; Fan, SZ; Lu, CW; Lin, TY; Abbod, MF; Shieh, JS |
| 2016 | A Comparison of Different Algorithms for EEG Signal Analysis for the Purpose of Monitoring Depth of Anesthesia | Liu, Q; Chen, YF; Fan, SZ; Abbod, MF; Shieh, JS |
| 2015 | Computational depth of anesthesia via multiple vital signs based on artificial neural networks | Sadrawi, M; Fan, SZ; Abbod, MF; Jen, KK; Shieh, JS |
| 2018 | Deep learning in classifying depth of anesthesia (DoA) | AlMeer, MH; Abbod, MF |
| 2016 | Improved spectrum analysis in EEG for measure of depth of anesthesia based on phase-rectified signal averaging | Quan, L; Chen, Y-F; Fan, S-Z; Abbod, M; Shieh, J-S |