Signal Analysis and Response Measurement

doi:10.1017/9781316443217.005

5 - Signal Analysis and Response Measurement

from Section 1 - Basic Principles

Published online by Cambridge University Press: 03 December 2019

Umberto Melia ,

Erik W. Jensen and

José F. Valencia

Edited by

Pedro L. Gambús and

Jan F. A. Hendrickx

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Pedro L. Gambús: Affiliation:
Hospital Clinic de Barcelona, Spain
Jan F. A. Hendrickx: Affiliation:
Aalst General Hospital, Belgium

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Summary

Monitoring during anaesthesia is based on the measurement of physiological signals that are recorded during surgical or other procedures requiring anaesthetic drug administration. The methods used to process these signals, calculate derived parameters and develop the different indicators of the physiological status of the patient or depth of anaesthesia are difficult to understand for anaesthesia providers without an engineering background. The present chapter aims to fill this gap by introducing the reader to the main concepts related to signal analysis commonly used to measure the response of human physiological systems under anaesthesia.

Type: Chapter
Information: Personalized Anaesthesia
Targeting Physiological Systems for Optimal Effect
, pp. 63 - 81

DOI: https://doi.org/10.1017/9781316443217.005 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2020

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References

Win, NN, Fukayama, H, Kohase, H, Umino, M: The different effects of intravenous propofol and midazolam sedation on hemodynamic and heart rate variability. Anaesth.Analges. 2005; 101: 97–102.Google Scholar

Rantanen, M: Novel multiparameter approach for measurement of nociception at skin incision during general anaesthesia. Br.J.Anaesth. 2006; 96: 367–76.CrossRef Google Scholar PubMed

Miller, RD: Miller’s Anesthesia: 1. Amsterdam: Elsevier, 2010.Google Scholar

Jensen, EW, Valencia, JF, López, A, Anglada, T, Agustí, M, Ramos, Y, Serra, R, Jospin, M, Pineda, P, Gambus, P: Monitoring hypnotic effect and nociception with two EEG-derived indices, qCON and qNOX, during general anaesthesia. Acta Anaesthesiol.Scand. 2014; 58: 933–41.Google Scholar

Viertiö-Oja, H, Maja, V, Särkelä, M, Talja, P, Tenkanen, N, Tolvanen-Laakso, H, Paloheimo, M, Vakkuri, A, Yli-Hankala, A, Meriläinen, P: Description of the Entropy algorithm as applied in the Datex-Ohmeda S/5 Entropy Module. Acta.Anaesthesiol.Scand. 2004; 48: 154–61.Google Scholar

Bouillon, TW: Pharmacodynamic interaction between propofol and remifentanil regarding hypnosis, tolerance of laryngoscopy, bispectral index, and electroencephalographic approximate entropy. Anesthesiology. 2004; 100: 1353–72.CrossRef Google Scholar PubMed

Jensen, EW, Lindholm, P, Henneberg, S: Auto regressive modeling with exogenous input of auditory evoked potentials to produce an on-line depth of anaesthesia index. Methods.Inf.Med. 1996; 35: 256–260.Google Scholar

Cohen, L: Time-Frequency Analysis. New Jersey: Prentice Hall, 1995.Google Scholar

Walling, PT, Kenneth, NH: Nonlinear changes in brain dynamics during emergence from sevoflurane anaesthesia: preliminary exploration using new software. Anesthesiology 2006; 105: 927–35.CrossRef Google Scholar PubMed

Lalitha, V, Eswaran, C: Automated detection of anesthetic depth levels using chaotic features with artificial neural networks. J.Med.Syst. 2007; 31: 445–52.Google Scholar

Natarajan, K, Acharya, R, Alias, F, Tiboleng, T, Puthusserypady, SK: Nonlinear analysis of EEG signals at different mental states. Biomed.Eng.OnLine. 2004; 3: 7–18.Google Scholar

Ferenets, R, Lipping, T, Anier, A, Jantti, V, Melto, S, Hovilehto, S: Comparison of entropy and complexity measures for the assessment of depth of sedation. IEEE Trans.Biomed.Eng. 2006; 53: 1067–77.CrossRef Google Scholar PubMed

Hornero, R, Abásolo, D, Escudero, J, Gómez, C: Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer’s disease. Philos.Trans.Royal.Soc. A. 2009; 367: 317–36.Google Scholar PubMed

Struys, MM, Jensen, EW, Smith, W, Smith, NT, Rampil, I, Dumortier, FJ, Mestach, C, Mortier, EP. Performance of the ARX-derived auditory evoked potential index as an indicator of anesthetic depth: a comparison with bispectral index and hemodynamic measures during propofol administration. Anesthesiology. 2002; 96: 803–16.CrossRef Google Scholar PubMed

Leslie, K, Sessler, DI, Smith, WD, Larson, MD, Ozaki, M, Blanchard, D, Crankshaw, DP: Prediction of movement during propofol/nitrous oxide anaesthesia: performance of concentration, electroencephalographic, pupillary, and hemodynamic indicators. Anesthesiology. 1996; 84: 52–63.Google Scholar

Kato, M, Komatsu, T, Kimura, T, Sugiyama, F, Nakashima, K, Shimada, Y: Spectral analysis of heart rate variability during isoflurane anaesthesia. Anesthesiology. 1992; 77: 669–74.Google Scholar

Sato, A, Sato, Y, Shimada, F, Torigata, Y: Varying changes in heart rate produced by nociceptive stimulation of the skin in rats at different temperatures. Brain.Res. 1976; 110: 301–11.Google Scholar

Jeanne, M, Logier, R, De Jonckheere, J, Tavernier, B: Heart rate variability during total intravenous anaesthesia: effects of nociception and analgesia. Auton.Neurosci. 2009; 147: 91–6.Google Scholar

Jeanne, M, Clément, C, De Jonckheere, J, Logier, R, Tavernier, B: Variations of the analgesia nociception index during general anaesthesia for laparoscopic abdominal surgery. J.Clin.Monit.Comput. 2012; 26: 289–94.Google Scholar

Jubran, A: Pulse oximetry. Crit.Care. 2015; 19: 1–7.Google Scholar

Smith, WD, Dutton, RC, Smith, NT: Measuring the performance of anesthetic depth indicators. Anesthesiology. 1996; 84: 38–51.Google Scholar

Gugino, LD, Chabot, RJ, Prichep, LS, John, ER, Formanek, V: Quantitative EEG changes associated with loss and return of consciousness in healthy adult volunteers anaesthetized with propofol or sevoflurane. Brit.J.Anaesth. 2001; 87: 421–8.CrossRef Google Scholar PubMed

Feshchenko, VA, Veselis, RA, Reinsel, RA: Propofol-induced alpha rhythm. Neuropsychobiology. 2004; 50: 257–66.Google Scholar

Hindriks, R, van Putten, MJ: Meanfield modeling of propofol-induced changes in spontaneous EEG rhythms. Neuroimage. 2002; 60: 2323–34.Google Scholar

Ching, S, Cimenser, A, Purdon, PL, Brown, EN, Kopell, NJ: Thalamocortical model for a propofol-induced alpha-rhythm associated with loss of consciousness. Proc.Natl.Acad.Sci.USA. 2010; 107: 22665–70.CrossRef Google Scholar PubMed

Melia, U, Vallverdú, M, Borrat, X, Valencia, JF, Jospin, M, Jensen, EW, Gambus, P, Caminal, P: Prediction of nociceptive responses during sedation by linear and non-linear measures of EEG signals in high frequencies. PloS.One. 2015; 10: e0123464.CrossRef Google Scholar PubMed

Valencia, JF, Melia, U, Vallverdú, M, Borrat, X, Jospin, M, Jensen, EW, Gambus, P, Caminal, P. Assessment of nociceptive responsiveness levels during sedation-analgesia by entropy analysis of EEG. Entropy. 2016; 18: 103–14.CrossRef Google Scholar

Guyon, I: An introduction to variable and feature selection. J.Mach.Learn.Res. 2003; 3: 1157–82.Google Scholar