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Journal of Clinical Monitoring and Computing
Published in: Journal of Clinical Monitoring and Computing 2/2020

01-04-2020 | Hypnosis in Dentistry | Original Research

Monitoring the level of hypnosis using a hierarchical SVM system

Authors: Ahmad Shalbaf, Reza Shalbaf, Mohsen Saffar, Jamie Sleigh

Published in: Journal of Clinical Monitoring and Computing | Issue 2/2020

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Abstract

Monitoring level of hypnosis is a major ongoing challenge for anesthetists to reduce anesthetic drug consumption, avoiding intraoperative awareness and prolonged recovery. This paper proposes a novel automated method for accurate assessing of the level of hypnosis with sevoflurane in 17 patients using the electroencephalogram signal. In this method, a set of distinctive features and a hierarchical classification structure based on support vector machine (SVM) methods, is proposed to discriminate the four levels of anesthesia (awake, light, general and deep states). The first stage of the hierarchical SVM structure identifies the awake state by extracting Shannon Permutation Entropy, Detrended Fluctuation Analysis and frequency features. Then deep state is identified by extracting the sample entropy feature; and finally light and general states are identified by extracting the three mentioned features of the first step. The accuracy of the proposed method of analyzing the brain activity during anesthesia is 94.11%; which was better than previous studies and also a commercial monitoring system (Response Entropy Index).
Literature
1.
Monk TG, Saini V, Weldon BC, Sigl JC. Anesthetic management and one-year mortality after noncardiac surgery. Anesth Analg. 2005;100:4–10.PubMed
2.
Sebel PS, Bowdle TA, Ghoneim MM, Rampil IJ, Padilla RE, Gan TJ, Domino KB. The incidence of awareness during anesthesia: a multicenter United States study. Anesth Analg. 2004;99:833–9.PubMed
3.
Gugino LD, Chabot RJ, Prichep LS, John ER, Formanek V, Aglio LS. Quantitative EEG changes associated with loss and return of consciousness in healthy adult volunteers anaesthetized with propofol or sevoflurane. Br J Anaesth. 2001;87:421–8.PubMed
4.
Viertio-Oja H, Maja V, Sarkela M, Talja P. Description of the entropy algorithm as applied in the Datex-Ohmeda S/5 entropy module. Acta Anaesthesiol Scand. 2004;48:154–61.PubMed
5.
Rampil IJ. A primer for EEG signal processing in anesthesia. Anesthesiology. 1998;89:980–1002.PubMed
6.
Kreuer S, Wilhelm W. The narcotrend monitor. Best Practice Res Clin Anaesth. 2006;20:111–9.
7.
Kortelainen J, Väyrynen E, Seppänen T. Depth of anesthesia during multidrug infusion: separating the effects of propofol and remifentanil using the spectral features of EEG. IEEE Trans Biomed Eng. 2011;58:1216–23.PubMed
8.
Nguyen-Ky T, Wen P, Li Y, Gray R. Measuring and reflecting depth of anesthesia using wavelet and power spectral density. IEEE Trans Inf Technol Biomed. 2011;15:630–9.PubMed
9.
Lalitha V, Eswaran C. Automated detection of anesthetic depth levels using chaotic features with artificial neural networks. J Med Syst. 2007;31:445–52.PubMed
10.
Zhang XS, Roy RJ, Jensen EW. EEG complexity as a measure of depth of anaesthesia for patients. IEEE Trans Biomed Eng. 2001;48:1424–33.PubMed
11.
Nguyen-Ky T, Wen P, Li Y. Consciousness and depth of anesthesia assessment based on bayesian analysis of EEG signals. IEEE Trans Biomed Eng. 2013;60:1488–98.PubMed
12.
Nguyen-Ky T, Wen P, Li Y. Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods. IET Signal Proc. 2014;8:907–17.
13.
Liu Q, Chen YF, Fan SZ, Abbod M, Shieh JS. Quasi-periodicities detection using phase-rectified signal averaging in EEG signals as a depth of anesthesia monitor. IEEE Trans Neural Syst Rehabil Eng. 2017;25:1773–84.PubMed
14.
Kuhlmann L, et al. tracking electroencephalographic changes using distributions of linear models: application to propofol-based depth of anesthesia monitoring. IEEE Trans Biomed Eng. 2017;64:870–81.PubMed
15.
Shalbaf R, Behnam H, Sleigh JW, Voss LJ. Using the Hilbert-Huang transform to measure the electroencephalographic effect of propofol. Physiol Meas. 2012;33:271–85.PubMed
16.
Shalbaf R, Behnam H, Sleigh JW, Steyn-Ross DA, SteynRoss ML. Frontal-temporal synchronization of EEG signals quantified by order patterns cross recurrence analysis during propofol anesthesia. IEEE Trans Neural Syst Rehabil Eng. 2015;23:468–74.PubMed
17.
Gifani P, Rabiee HR, Hashemi MH, Zadeh MS, Taslimi P, Ghanbari M. Optimal fractal-scaling analysis of human EEG dynamic for depth of anesthesia quantification. J Franklin Inst. 2007;344:212–29.
18.
Jospin M, Caminal P, Jensen EW, et al. Detrended fluctuation analysis of EEG as a measure of depth of anaesthesia. IEEE Trans Biomed Eng. 2007;54:840–6.PubMed
19.
Ferenets R, Lipping T, Anier A, Jantti V, Melto S, Hovilehto S. Comparison of entropy and complexity measures for the assessment of depth of a sedation. IEEE Trans Biomed Eng. 2006;53:1067–77.PubMed
20.
Shalbaf R, Behnam H, Sleigh JW, Voss LJ. Measuring the effects of sevoflurane on electroencephalogram using sample entropy. Acta Anaesthesiol Scand. 2012;56:880–9.PubMed
21.
Shalbaf R, Behnam H, Sleigh JW, Steyn-Ross A, Voss LJ. Monitoring the depth of anesthesia using entropy features and an artificial neural network. J Neurosci Methods. 2013;218:17–24.PubMed
22.
Liang Z, Wang Y, Sun X, Li D, Voss LJ, Sleigh JW, Hagihira S, Li X. EEG entropy measures in anesthesia. Front Comput Neurosci. 2015;18:9–16.
23.
Mirsadeghi M, Behnam H, Shalbaf R, Jelveh Moghadam H. Characterizing awake and anesthetized states using a dimensionality reduction method. J Med Syst. 2016;40:13.PubMed
24.
Chen D, Li D, Xiong M, Bao H, Li X. GPGPU-aided ensemble empirical-mode decomposition for EEG analysis during anesthesia. IEEE Trans Inf Technol Biomed. 2010;14:1417–27.PubMed
25.
Kortelainen J, Väyrynen E, Seppänen T. Isomap approach to EEG-based assessment of neurophysiological changes during anesthesia. IEEE Trans Neural Syst Rehabil Eng. 2011;19:113–20.PubMed
26.
Shalbaf R, Behnam H, Jelveh Moghadam H. Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables. Cogn Neurodyn. 2014;8:1–11.
27.
Li T, Wen P. Depth of anaesthesia assessment using interval second-order difference plot and permutation entropy techniques. IET Signal Proc. 2017;11:221–7.
28.
Zhang XS, Roy RJ. Derived fuzzy knowledge model for estimating the depth of anesthesia. IEEE Trans Biomed Eng. 2001;48(3):312–23.PubMed
29.
Shalbaf A, Saffar M, Sleigh JW, Shalbaf R. Monitoring the depth of anesthesia using a new adaptive neuro-fuzzy system. IEEE J Biomed Health Inf. 2018;22:671–7.
30.
Esmaeili V, Assareh A, Shamsollahi MB, Moradi MH, Arefian NM. Estimating the depth of anesthesia using fuzzy soft computation applied to EEG features. Intell Data Anal. 2008;12:393–407.
31.
Sela Y, Freiman M, Dery E, Edrei Y, Safadi R, Pappo O, Joskowicz L, Abramovitch R. fMRI-based hierarchical SVM model for the classification and grading of liver fibrosis. IEEE Trans Biomed Eng. 2011;58:2574–81.PubMed
32.
Sekar BD, Chui Dong M, Shi J, Hu XY. Fused hierarchical neural networks for cardiovascular disease diagnosis. IEEE Sens J. 2012;12:644–50.
33.
Selver MA, Akay O, Ardali E, Yavuz AB, Onal O, Ozden G. Cascaded and hierarchical neural networks for classifying surface images of marble slabs. IEEE Trans Syst Man Cybern. 2009;39:426–39.
34.
Li Y, Du L, Liu H. Hierarchical classification of moving vehicles based on empirical mode decomposition of micro-doppler signatures. IEEE Trans Geosci Remote Sens. 2013;51:3001–13.
35.
Vanitha L, Suresh GR (2014) Hierarchical SVM to detect mental stress in human beings using Heart Rate Variability. In: 2nd international conference on devices, circuits and systems, pp 1–5
36.
Acir N, Oztura I, Kuntalp M, Baklan B, Guzelis C. Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks. IEEE Trans Biomed Eng. 2005;52(1):30–40.PubMed
37.
Chen K. On the use of different speech representations for speaker modeling. IEEE Trans Syst Man Cybern C. 2005;35(3):301–14.
38.
Chen K. A connectionist method for pattern classification with diverse features. Pattern Recognit Lett. 1998;19(7):545–58.
39.
Mckay ID, Voss LJ, Sleigh JW, Barnard JP, Johannsen EK. Pharmacokinetic pharmacodynamic modeling the hypnotic effect of sevoflurane using the spectral entropy of the electroencephalogram. Anesth Analg. 2006;102:91–7.PubMed
40.
Chander D, García PS, MacColl JN, Illing S, Sleigh JW. Electroencephalographic variation during end maintenance and emergence from surgical anesthesia. PLoS ONE. 2014;9(9):e106291.PubMedPubMedCentral
41.
Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 2000;278:121–8.
42.
Bandt C, Pompe B. Permutation entropy: a natural complexity measure for time series. Phys Rev Lett. 2002;88:174102.PubMed
43.
Bertsekas DP. Nonlinear programming. Belmont, MA: Athenas Scientific; 1995.
Metadata
Title
Monitoring the level of hypnosis using a hierarchical SVM system
Authors
Ahmad Shalbaf
Reza Shalbaf
Mohsen Saffar
Jamie Sleigh
Publication date
01-04-2020
Publisher
Springer Netherlands
Published in
Journal of Clinical Monitoring and Computing / Issue 2/2020
Print ISSN: 1387-1307
Electronic ISSN: 1573-2614
DOI
https://doi.org/10.1007/s10877-019-00311-1

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