Application of multivariate empirical mode decomposition and sample entropy in EEG signals via artificial neural networks for interpreting depth of anesthesia

Entropy

Volumn 15, Issue 9, 2013, Pages 3325-3339

  Huang, Jeng Rung ^a   Fan, Shou Zen ^b   Abbod, Maysam F ^c   Jen, Kuo Kuang ^d   Wu, Jeng Fu ^d   Shieh, Jiann Shing ^a,e  

^a YUAN ZE UNIVERSITY   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^c BRUNEL UNIVERSITY   (United Kingdom)

^d CHUNG SHAN INSTITUTE OF SCIENCE AND TECHNOLOGY   (Taiwan)

^e NATIONAL CENTRAL UNIVERSITY   (Taiwan)

Author keywords

Artificial neural networks; Depth of anesthesia; Electroencephalography; Multivariate empirical mode decomposition; Receiver operating characteristic curve; Sample entropy

Indexed keywords

EID: 84885363168     PISSN: None     EISSN: 10994300     Source Type: Journal    
DOI: 10.3390/e15093325     Document Type: Article

References (33)

1. Kent, C.D; Domino, K.B. Depth of anesthesia. Curr. Opin. Anaesthesiol. 2009, 22, 782-787.
2. Jeannea, M.; Logierb, R.; Jonckheereb, J.D.; Tavernier, B. Heart rate variability during total intravenous anesthesia: Effects of nociception and analgesia. Auton. Neurosci. 2009, 147, 91-96.
3. Pomfrett, C.J.D.; Dolling, S.; Anders, N.R.K.; Glover, D.G; Bryan, A.; Pollard, B.J. Delta sleep-inducing peptide alters bispectral index, the electroencephalogram and heart rate variability when used as an adjunct to isoflurane anaesthesia. Eur. J. Anaesthesiol. 2009, 26, 128-134.
4. Ottoa, K.A.; Cebotarib, S.; Höfflerb, H.K.; Tudoracheb, I. Electroencephalographic Narcotrend index, spectral edge frequency and median power frequency as guide to anaesthetic depth for cardiac surgery in laboratory sheep. Vet. J. 2012, 191, 354-359.
5. Niemarkt, H.; Jennekens, J.W.; Pasman, J.W.; Katgert, T.; Pul, C.V.; Gavilanes, A.W.D.; Kramer, B.W.; Zimmermann, L.J.; Oetomo, S.B.; Andriessen, P. Maturational changes in automated EEG spectral power analysis in preterm infants. Pediatr. Res. 2011, 70, 529-534.
6. Crüts, B.; van Etten, L.; Törnqvist, H.; Blomberg, A.; Sandström, T.; Mills, N.L.; Borm, P.J.A. Exposure to diesel exhaust induces changes in EEG in human volunteers. Part. Fibre Toxicol. 2008, 5, 1-6.
7. Schultz, A.; Siedenberg, M.; Grouven, U.; Kneif, T.; Schultz, B. Comparison of narcotrend index, bispectral index, spectral and entropy parameters during induction of propofol-remifentanil anaesthesia. J. Clin. Monit. Comput. 2008, 22, 103-111.
8. Stam, C.J. Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field. Clin. Neurophysiol. 2005, 116, 2266-2301.
9. Aho1, A.J.; Yli-Hankala, A.; Lyytikäinen, L.-P.; Jäntti, V. Facial muscle activity, response entropy, and state entropy indices during noxious stimuli in propofol-nitrous oxide or propofol-nitrous oxide-Remifentanil anaesthesia without neuromuscular block. Br. J. Anaesth. 2009, 102, 227-233.
10. Clanet, M.; Bonhomme, V.; Lhoest, L.; Born, J.D.; Hans, P. Unexpected entropy response to saline spraying at the end of posterior fossa surgery: A few cases report. Acta Anaesthesiol. Belg. 2011, 62, 87-90.
11. Höcker, J.; Raitschew, B.; Meybohm, P.; Broch, O.; Stapelfeldt, C.; Gruenewald, M.; Cavus, E.; Steinfath, M.; Bein, B. Differences between bispectral index and spectral entropy during xenon anaesthesia: A comparison with propofol anaesthesia. Anaesthesia 2010, 65, 595-600.
12. Jian, X.; Wei, R.; Zhan, T.; Gu, Q. Using the concept of chous pseudo amino acid composition to predict apoptosis proteins subcellular location: An approach by approximate entropy. Protein Pept. Lett. 2008, 15, 392-396.
13. Ocak, H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 2009, 36, 2027-2036.
14. Ramdania, S.; Seiglea, B.; Lagardea, J.; Boucharab, F.; Bernarda, P.L. On the use of sample entropy to analyze human postural sway data. Med. Eng. Phys. 2009, 31, 1023-1031.
15. Alcaraza, R.; Rieta, J.J. Sample entropy of the main atrial wave predicts spontaneous termination of paroxysmal atrial fibrillation. Med. Eng. Phys. 2009, 31, 917-922.
16. Ramdani, S.; Bouchara, F.; Lagarde, J. Influence of noise on the sample entropy algorithm. Chaos 2009, 19, 013123.
17. Govindan, R.; Wilsona, J.; Eswaranb, H.; Loweryb, C.; Preialb, H. Revisiting sample entropy analysis. Phys. A: Stat. Mech. Appl. 2007, 376, 158-164.
18. Wang, H.; Zheng, W. Local temporal common spatial patterns for robust single-trial EEG classification. IEEE Trans. Neural Syst. Rehabil. Eng. 2008, 16, 131-139.
19. Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H.H.; Zheng, Q.; Yen, N.C.; Tung, C.C.; Liu, H.H. The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. R. Soc. Lond. 1998, 454, 903-995.
20. Wu, Z.; Huang, N.E. On the filtering properties of the empirical mode decomposition. Adv. Adap. Data Anal. 2010, 2, 397-414.
21. Wu, Z.; Huang, N.E. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv. Adap. Data Anal. 2009, 1, 1-41.
22. Rehman, N.; Mandic, D.P. Multivariate empirical mode decomposition. Proc. R. Soc. A: Math. Phys. Eng. Sci. 2010, 466, 1291-1302.
23. Rehman, N.; Mandic, D.P. Filter bank property of multivariate empirical mode decomposition. IEEE Trans. Signal Process. 2011, 59, 2421-2426.
24. Lin, C.T.; Lee, C.S.G. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems; Prentice Hall: Upper Saddle River, NJ, USA, 1996; Chapter 9, 205-223.
25. Barrya, R.J.; Clarkea, A.R.; Johnstonea, S.J.; McCarthyb, R.; Selikowitzb, M. Electroencephalogram Θ/β ratio and arousal in attention-deficit/hyperactivity disorder: Evidence of independent processes. Biol. Psychiatry 2009, 31, 398-401.
26. Cook, N.R. Statistical evaluation of prognostic versus diagnostic models: beyond the roc curve. Clin. Chem. 2008, 54, 17-23.
27. Pencina, M.J.; D'Agostino, R.B.; Vasan, R.S. Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Stat. Med. 2008, 27, 157-172.
28. Yeh, J.R.; Shieh, J.S.; Huang, N.E. Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. Adv. Adapt. Data Anal. 2010, 2, 135-156.
29. Fleureau, J.; Kachenoura, A.; Nunes, J.C.; Albera, L.; Senhadji, L. Multivariate empirical mode decomposition and application to multichannel filtering. Signal Process. 2011, 91, 2783-2792.
30. Fleureau, J.; Nunes, J.C.; Kachenoura, A.; Albera, L.; Senhadji, L. Turning tangent empirical mode decomposition: A framework for mono and multivariate signals. IEEE Trans. Signal Process. 2011, 59, 1309-1316.
31. Lee, P.L.; Hsieh, J.C.; Wu, C.H.; Shyu, K.K.; Chen, S.S.; Yeh, T.C.; Wu, Y.T. The brain computer interface using flash visual evoked potential and independent component analysis. Ann. Biomed. Eng. 2006, 34, 1641-1654.
32. Li, L.; Chen, J.-H. Emotion Recognition Using Physiological Signals from Multiple Subjects. In Proceedings of International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP'06), Pasadena, CA, USA, 18-20 December 2006; pp. 355-358.
33. Barr, G.; Anderson, R.E.; Öwall, A.; Jakobsson, J.G. Being awake intermittently during propofol-induced hypnosis: A study of BIS, explicit and implicit memory. Acta Anaesthesiol. Scand. 2001, 45, 834-838.