299 related articles for article (PubMed ID: 31159263)
21. Improved spectrum analysis in EEG for measure of depth of anesthesia based on phase-rectified signal averaging.
Liu Q; Chen YF; Fan SZ; Abbod MF; Shieh JS
Physiol Meas; 2017 Feb; 38(2):116-138. PubMed ID: 28033111
[TBL] [Abstract][Full Text] [Related]
22. [Study on the Evaluation Index of Depth of Anesthesia Awareness Based on Sample Entropy and Decision Tree].
Liu J; Zhou Y; Chen S; Xu T; Chen X; Xie F
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2015 Apr; 32(2):434-9. PubMed ID: 26211267
[TBL] [Abstract][Full Text] [Related]
23. Quasi-Periodicities Detection Using Phase-Rectified Signal Averaging in EEG Signals as a Depth of Anesthesia Monitor.
Liu Q; Chen YF; Fan SZ; Abbod MF; Shieh JS
IEEE Trans Neural Syst Rehabil Eng; 2017 Oct; 25(10):1773-1784. PubMed ID: 28391200
[TBL] [Abstract][Full Text] [Related]
24. EEG complexity as a measure of depth of anesthesia for patients.
Zhang XS; Roy RJ; Jensen EW
IEEE Trans Biomed Eng; 2001 Dec; 48(12):1424-33. PubMed ID: 11759923
[TBL] [Abstract][Full Text] [Related]
25. Derived fuzzy knowledge model for estimating the depth of anesthesia.
Zhang XS; Roy RJ
IEEE Trans Biomed Eng; 2001 Mar; 48(3):312-23. PubMed ID: 11327499
[TBL] [Abstract][Full Text] [Related]
26. Multiscale permutation entropy analysis of EEG recordings during sevoflurane anesthesia.
Li D; Li X; Liang Z; Voss LJ; Sleigh JW
J Neural Eng; 2010 Aug; 7(4):046010. PubMed ID: 20581428
[TBL] [Abstract][Full Text] [Related]
27. Monitors of the hypnotic component of anesthesia - correlation between bispectral index and cerebral state index.
Pilge S; Kreuzer M; Kochs EF; Zanner R; Paprotny S; Schneider G
Minerva Anestesiol; 2012 Jun; 78(6):636-45. PubMed ID: 22310192
[TBL] [Abstract][Full Text] [Related]
28. Comparative evaluation of the Datex-Ohmeda S/5 Entropy Module and the Bispectral Index monitor during propofol-remifentanil anesthesia.
Schmidt GN; Bischoff P; Standl T; Hellstern A; Teuber O; Schulte Esch J
Anesthesiology; 2004 Dec; 101(6):1283-90. PubMed ID: 15564934
[TBL] [Abstract][Full Text] [Related]
29. Automated EEG preprocessing during anaesthesia: new aspects using artificial neural networks.
Jeleazcov C; Egner S; Bremer F; Schwilden H
Biomed Tech (Berl); 2004 May; 49(5):125-31. PubMed ID: 15212197
[TBL] [Abstract][Full Text] [Related]
30. Analysis of depth of anesthesia with Hilbert-Huang spectral entropy.
Li X; Li D; Liang Z; Voss LJ; Sleigh JW
Clin Neurophysiol; 2008 Nov; 119(11):2465-75. PubMed ID: 18812265
[TBL] [Abstract][Full Text] [Related]
31. Depth of anaesthesia assessment based on adult electroencephalograph beta frequency band.
Li T; Wen P
Australas Phys Eng Sci Med; 2016 Sep; 39(3):773-81. PubMed ID: 27323760
[TBL] [Abstract][Full Text] [Related]
32. A comparison of different synchronization measures in electroencephalogram during propofol anesthesia.
Liang Z; Ren Y; Yan J; Li D; Voss LJ; Sleigh JW; Li X
J Clin Monit Comput; 2016 Aug; 30(4):451-66. PubMed ID: 26350675
[TBL] [Abstract][Full Text] [Related]
33. Monitoring Depth of Anesthesia Based on Hybrid Features and Recurrent Neural Network.
Li R; Wu Q; Liu J; Wu Q; Li C; Zhao Q
Front Neurosci; 2020; 14():26. PubMed ID: 32116494
[TBL] [Abstract][Full Text] [Related]
34. Entropy of the EEG in transition to burst suppression in deep anesthesia: Surrogate analysis.
Anier A; Lipping T; Jantti V; Puumala P; Huotari AM
Annu Int Conf IEEE Eng Med Biol Soc; 2010; 2010():2790-3. PubMed ID: 21095969
[TBL] [Abstract][Full Text] [Related]
35. A Randomized Controlled Trial Comparison of NeuroSENSE and Bispectral Brain Monitors During Propofol-Based Versus Sevoflurane-Based General Anesthesia.
Bresson J; Gayat E; Agrawal G; Chazot T; Liu N; Hausser-Haw C; Fischler M
Anesth Analg; 2015 Nov; 121(5):1194-201. PubMed ID: 26489054
[TBL] [Abstract][Full Text] [Related]
36. Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states.
Zhan J; Wu ZX; Duan ZX; Yang GY; Du ZY; Bao XH; Li H
BMC Anesthesiol; 2021 Mar; 21(1):66. PubMed ID: 33653263
[TBL] [Abstract][Full Text] [Related]
37. Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal.
Sanjari N; Shalbaf A; Shalbaf R; Sleigh J
Basic Clin Neurosci; 2021; 12(2):269-280. PubMed ID: 34925723
[TBL] [Abstract][Full Text] [Related]
38. Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks.
Sadrawi M; Fan SZ; Abbod MF; Jen KK; Shieh JS
Biomed Res Int; 2015; 2015():536863. PubMed ID: 26568957
[TBL] [Abstract][Full Text] [Related]
39. Consciousness and depth of anesthesia assessment based on Bayesian analysis of EEG signals.
Nguyen-Ky T; Wen PP; Li Y
IEEE Trans Biomed Eng; 2013 Jun; 60(6):1488-98. PubMed ID: 23314762
[TBL] [Abstract][Full Text] [Related]
40. Evaluation of entropy for monitoring the depth of anesthesia compared with bispectral index: a multicenter clinical trial.
Gao JD; Zhao YJ; Xu CS; Zhao J; Huang YG; Wang TL; Pei L; Wang J; Yao LN; Ding Q; Tan ZM; Zhu ZR; Yue Y
Chin Med J (Engl); 2012 Apr; 125(8):1389-92. PubMed ID: 22613640
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]