321 related articles for article (PubMed ID: 30338496)
1. Automatic anesthesia depth staging using entropy measures and relative power of electroencephalogram frequency bands.
Jahanseir M; Setarehdan SK; Momenzadeh S
Australas Phys Eng Sci Med; 2018 Dec; 41(4):919-929. PubMed ID: 30338496
[TBL] [Abstract][Full Text] [Related]
2. Differences between state entropy and bispectral index during analysis of identical electroencephalogram signals: a comparison with two randomised anaesthetic techniques.
Pilge S; Kreuzer M; Karatchiviev V; Kochs EF; Malcharek M; Schneider G
Eur J Anaesthesiol; 2015 May; 32(5):354-65. PubMed ID: 25564779
[TBL] [Abstract][Full Text] [Related]
3. Monitoring the Depth of Anesthesia Using a New Adaptive Neurofuzzy System.
Shalbaf A; Saffar M; Sleigh JW; Shalbaf R
IEEE J Biomed Health Inform; 2018 May; 22(3):671-677. PubMed ID: 28574372
[TBL] [Abstract][Full Text] [Related]
4. A wavelet transform based method to determine depth of anesthesia to prevent awareness during general anesthesia.
Mousavi SM; Adamoğlu A; Demiralp T; Shayesteh MG
Comput Math Methods Med; 2014; 2014():354739. PubMed ID: 25276220
[TBL] [Abstract][Full Text] [Related]
5. Emergence EEG pattern classification in sevoflurane anesthesia.
Liang Z; Huang C; Li Y; Hight DF; Voss LJ; Sleigh JW; Li X; Bai Y
Physiol Meas; 2018 Apr; 39(4):045006. PubMed ID: 29513276
[TBL] [Abstract][Full Text] [Related]
6. Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia.
Gu Y; Liang Z; Hagihira S
Sensors (Basel); 2019 May; 19(11):. PubMed ID: 31159263
[TBL] [Abstract][Full Text] [Related]
7. Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier.
Al-Salman W; Li Y; Wen P
Neurosci Res; 2021 Nov; 172():26-40. PubMed ID: 33965451
[TBL] [Abstract][Full Text] [Related]
8. [A monitoring technique in detecting the depth of anesthesia by bispectral index].
Le JW; Mo GM; Lin M
Zhongguo Yi Liao Qi Xie Za Zhi; 2005 Jul; 29(5):321-4. PubMed ID: 16419932
[TBL] [Abstract][Full Text] [Related]
9. Efficient sleep classification based on entropy features and a support vector machine classifier.
Zhang Z; Wei S; Zhu G; Liu F; Li Y; Dong X; Liu C; Liu F
Physiol Meas; 2018 Nov; 39(11):115005. PubMed ID: 30475743
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. BIS and Entropy in the elderly.
Arnold G; Kluger M; Voss L; Sleigh J
Anaesthesia; 2007 Sep; 62(9):907-12. PubMed ID: 17697217
[TBL] [Abstract][Full Text] [Related]
12. Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals.
Acharya UR; Sree SV; Alvin AP; Yanti R; Suri JS
Int J Neural Syst; 2012 Apr; 22(2):1250002. PubMed ID: 23627588
[TBL] [Abstract][Full Text] [Related]
13. Increase in high frequency EEG activity explains the poor performance of EEG spectral entropy monitor during S-ketamine anesthesia.
Maksimow A; Särkelä M; Långsjö JW; Salmi E; Kaisti KK; Yli-Hankala A; Hinkka-Yli-Salomäki S; Scheinin H; Jääskeläinen SK
Clin Neurophysiol; 2006 Aug; 117(8):1660-8. PubMed ID: 16807101
[TBL] [Abstract][Full Text] [Related]
14. Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.
Kumar S; Ghosh S; Tetarway S; Sinha RK
Med Biol Eng Comput; 2015 Jul; 53(7):609-22. PubMed ID: 25773367
[TBL] [Abstract][Full Text] [Related]
15. A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.
Zarei R; He J; Siuly S; Zhang Y
Comput Methods Programs Biomed; 2017 Jul; 146():47-57. PubMed ID: 28688489
[TBL] [Abstract][Full Text] [Related]
16. [Automatic analysis of electroencephalogram: what is its value in the year 2000 for monitoring anesthesia depth?].
Billard V; Constant I
Ann Fr Anesth Reanim; 2001 Nov; 20(9):763-85. PubMed ID: 11759318
[TBL] [Abstract][Full Text] [Related]
17. [Monitoring the depth of anesthesia using a fuzzy neural network based on EEG].
Li M; Ye ZQ
Zhongguo Yi Liao Qi Xie Za Zhi; 2006 Jul; 30(4):253-5. PubMed ID: 17039930
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Spectral and Entropic Features Are Altered by Age in the Electroencephalogram in Patients under Sevoflurane Anesthesia.
Kreuzer M; Stern MA; Hight D; Berger S; Schneider G; Sleigh JW; García PS
Anesthesiology; 2020 May; 132(5):1003-1016. PubMed ID: 32108685
[TBL] [Abstract][Full Text] [Related]
20. Monitoring the depth of anesthesia using entropy features and an artificial neural network.
Shalbaf R; Behnam H; Sleigh JW; Steyn-Ross A; Voss LJ
J Neurosci Methods; 2013 Aug; 218(1):17-24. PubMed ID: 23567809
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
