Skip to main content

Advertisement

SpringerLink
Log in

Photoplethysmography-derived approximate entropy and sample entropy as measures of analgesia depth during propofol–remifentanil anesthesia

  • Original Research
  • Published:
Journal of Clinical Monitoring and Computing Aims and scope Submit manuscript

Abstract

The ability to monitor the physiological effect of the analgesic agent is of interest in clinical practice. Nonstationary changes would appear in photoplethysmography (PPG) during the analgesics-driven transition to analgesia. The present work studied the properties of nonlinear methods including approximate entropy (ApEn) and sample entropy (SampEn) derived from PPG responding to a nociceptive stimulus under various opioid concentrations. Forty patients with ASA I or II were randomized to receive one of the four possible remifentanil effect-compartment target concentrations (Ceremi) of 0, 1, 3, and 5 ng·ml−1 and a propofol effect-compartment target-controlled infusion to maintain the state entropy (SE) at 50 ± 10. Laryngeal mask airway (LMA) insertion was applied as a standard noxious stimulation. To optimize the performance of ApEn and SampEn, different coefficients were carefully evaluated. The monotonicity of ApEn and SampEn changing from low Ceremi to high Ceremi was assessed with prediction probabilities (PK). The result showed that low Ceremi (0 and 1 ng·ml−1) could be differentiated from high Ceremi (3 and 5 ng·ml−1) by ApEn and SampEn. Depending on the coefficient employed in algorithm: ApEn with k = 0.15 yielded the largest PK value (0.875) whereas SampEn gained its largest PK of 0.867 with k = 0.2. Thus, PPG-based ApEn and SampEn with appropriate k values have the potential to offer good quantification of analgesia depth under general anesthesia.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Eger EI, Sonner JM. Anaesthesia defined (gentlemen, this is no humbug). Best Pract Res Clin Anaesthesiol. 2006;20:23–9.

    Article  Google Scholar 

  2. Guignard B. Monitoring analgesia. Best Pract Res Clin Anaesthesiol. 2006;20:161–80.

    Article  Google Scholar 

  3. Fechner J, Ihmsen H, Schuttler J, et al. The impact of intra-operative sufentanil dosing on post-operative pain, hyperalgesia and morphine consumption after cardiac surgery. Eur J Pain. 2013;17:562–70.

    Article  CAS  Google Scholar 

  4. Steyaert A, De Kock M. Chronic postsurgical pain. Curr Opin Anesthesiol. 2012;25:584–8.

    Article  Google Scholar 

  5. Gruenewald M, Ilies C. Monitoring the nociception-anti-nociception balance. Best Pract Res Clin Anaesthesiol. 2013;27:235–47.

    Article  Google Scholar 

  6. Korhonen I, Yli-Hankala A. Photoplethysmography and nociception. Acta Anaesthesiol Scand. 2009;53:975–85.

    Article  CAS  Google Scholar 

  7. Huiku M, Uutela K, van Gils M, et al. Assessment of surgical stress during general anaesthesia. Br J Anaesth. 2007;98:447–55.

    Article  CAS  Google Scholar 

  8. Bonhomme V, Uutela K, Hans G, et al. Comparison of the Surgical Pleth Index (TM) with haemodynamic variables to assess nociception-anti-nociception balance during general anaesthesia. Br J Anaesth. 2011;106:101–11.

    Article  CAS  Google Scholar 

  9. Edry R, Recea V, Dikust Y, et al. Preliminary intraoperative validation of the nociception level index a noninvasive nociception monitor. Anesthesiology. 2016;125:193–203.

    Article  Google Scholar 

  10. Choi BM, Park C, Lee YH, et al. Development of a new analgesic index using nasal photoplethysmography. Anaesthesia. 2018;73:1123–30.

    Article  CAS  Google Scholar 

  11. Ni ZQ, Wang L, Meng J, et al. EEG signal processing in anesthesia feature extraction of time and frequency parameters. Procedia Environ Sci. 2011;8:215–20.

    Article  Google Scholar 

  12. Jordan D, Stockmanns G, Kochs EF, et al. Electroencephalographic order pattern analysis for the separation of consciousness and unconsciousness an analysis of approximate entropy, permutation entropy, recurrence rate, and phase coupling of order recurrence plots. Anesthesiology. 2008;109:1014–22.

    Article  Google Scholar 

  13. Wei Q, Liu Q, Fan SZ, et al. Analysis of EEG via multivariate empirical mode decomposition for depth of anesthesia based on sample entropy. Entropy. 2013;15:3458–70.

    Article  Google Scholar 

  14. Kreuzer M. EEG based monitoring of general anesthesia: taking the next steps. Front Comput Neurosci. 2017;11:Art 56.

    Article  Google Scholar 

  15. Koskinen M, Seppanen T, Tong SB, et al. Monotonicity of approximate entropy during transition from awareness to unresponsiveness due to propofol anesthetic induction. IEEE Trans Biomed Eng. 2006;53:669–75.

    Article  Google Scholar 

  16. Vakkuri A, Yli-Hankala A, Talja P, et al. Time-frequency balanced spectral entropy as a measure of anesthetic drug effect in central nervous system during sevoflurane, propofol, and thiopental anesthesia. Acta Anaesthesiol Scand. 2004;48:145–53.

    Article  CAS  Google Scholar 

  17. Schnider TW, Minto CF, Gambus PL, et al. The influence of method of administration and covariates on the pharmacokinetics of propofol in adult volunteers. Anesthesiology. 1998;88:1170–82.

    Article  CAS  Google Scholar 

  18. Minto CF, Schnider TW, Egan TD, et al. Influence of age and gender on the pharmacokinetics and pharmacodynamics of remifentanil. I. Model development. Anesthesiology. 1997;86:10–23.

    Article  CAS  Google Scholar 

  19. Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA. 1991;88:2297–301.

    Article  CAS  Google Scholar 

  20. Bruhn J, Ropcke H, Hoeft A. Approximate entropy as an electroencephalographic measure of anesthetic drug effect during desflurane anesthesia. Anesthesiology. 2000;92:715–26.

    Article  CAS  Google Scholar 

  21. Liu Q, Chen YF, Fan SZ, et al. A comparison of five different algorithms for EEG signal analysis in artifacts rejection for monitoring depth of anesthesia. Biomed Signal Process Control. 2016;25:24–34.

    Article  Google Scholar 

  22. Pincus SM, Goldberger AL. Physiological time-series analysis: what does regularity quantify. Am J Physiol. 1994;266:H1643–H16561656.

    CAS  PubMed  Google Scholar 

  23. Kortelainen J, Koskinen M, Mustola S, et al. Effect of remifentanil on the nonlinear electroencephalographic entropy parameters in propofol anesthesia. In: Conf Proc IEEE Eng Med Biol Soc, 2009. p. 4994–7.

  24. Shalbaf R, Behnam H, Sleigh J, et al. Measuring the effects of sevoflurane on electroencephalogram using sample entropy. Acta Anaesthesiol Scand. 2012;56:880–9.

    Article  CAS  Google Scholar 

  25. Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 2000;278:H2039–H20492049.

    Article  CAS  Google Scholar 

  26. Liang ZH, Wang YH, Sun X, et al. EEG entropy measures in anesthesia. Front Comput Neurosci. 2015;9:Art 16.

    Article  Google Scholar 

  27. Shalbaf R, Behnam H, Sleigh JW, et al. Monitoring the depth of anesthesia using entropy features and an artificial neural network. J Neurosci Methods. 2013;218:17–24.

    Article  Google Scholar 

  28. Rantanen M, Yli-Hankala A, van Gils M, et al. Novel multiparameter approach for measurement of nociception at skin incision during general anaesthesia. Br J Anaesth. 2006;96:367–76.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  30. Shoushtarian M, Sahinovic MM, Absalom AR, et al. Comparisons of electroencephalographically derived measures of hypnosis and antinociception in response to standardized stimuli during target-controlled propofol–remifentanil anesthesia. Anesth Analg. 2016;122:382–92.

    Article  CAS  Google Scholar 

  31. Martini CH, Boon M, Broens SJL, et al. Ability of the Nociception Level, a multiparameter composite of autonomic signals, to detect noxious stimuli during propofol–remifentanil anesthesia. Anesthesiology. 2015;123:524–34.

    Article  CAS  Google Scholar 

  32. Weil G, Passot S, Servin F, et al. Does spectral entropy reflect the response to intubation or incision during propofol–remifentanil anesthesia. Anesth Analg. 2008;106:152–9.

    Article  CAS  Google Scholar 

  33. von Dincklage F, Correll C, Schneider MHN, et al. Utility of nociceptive flexion reflex threshold, bispectral index, composite variability index and noxious stimulation response index as measures for nociception during general anaesthesia. Anaesthesia. 2012;67:899–905.

    Article  Google Scholar 

  34. Gruenewald M, Ilies C, Herz J, et al. Influence of nociceptive stimulation on analgesia nociception index (ANI) during propofol–remifentanil anaesthesia. Br J Anaesth. 2013;110:1024–30.

    Article  CAS  Google Scholar 

  35. Renaud-Roy E, Stöckle P-A, Maximos S, et al. Correlation between incremental remifentanil doses and the Nociception Level (NOL) index response after intraoperative noxious stimuli. Can J Anesth. 2019;66:1049–61.

    Article  Google Scholar 

  36. Funcke S, Sauerlaender S, Pinnschmidt HO, et al. Validation of innovative techniques for monitoring nociception during general anesthesia a clinical study using tetanic and intracutaneous electrical stimulation. Anesthesiology. 2017;127:272–83.

    Article  Google Scholar 

Download references

Funding

This study was supported by the National Natural Science Foundation of China (Grant No: 81870868), Major Scientific Project of Zhejiang Lab (Grant No: 2018DG0ZX01) and China’s Natural Science Foundation #31627802.

Author information

Authors and Affiliations

  1. College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China

    Wanlin Chen, Feng Jiang, Jiajun Miao, Shali Chen & Hang Chen

  2. Key Lab of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China

    Wanlin Chen, Feng Jiang, Jiajun Miao, Shali Chen & Hang Chen

  3. Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Hangzhou, China

    Wanlin Chen, Feng Jiang, Xinzhong Chen, Jiajun Miao, Shali Chen & Hang Chen

  4. Department of Anesthesia, Women’s Hospital School of Medicine Zhejiang University, Hangzhou, China

    Xinzhong Chen, Ying Feng & Cuicui Jiao

  5. Connected Healthcare Big Data Research Center, Zhejiang Lab, Hangzhou, China

    Hang Chen

Authors
  1. Wanlin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feng Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinzhong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiajun Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shali Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Cuicui Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

WC was responsible for study design, data analysis, manuscript preparation and revision. FJ contributed to the study design, data analysis and revised the manuscript for important intellectual content. XC and HC were involved in the study design, data collection, manuscript revision and project supervision. YF and CJ were responsible for data collection and interpretation of data. JM and SC contributed to data analysis and critical revision of the manuscript for important intellectual content. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hang Chen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed on human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the Research Ethics Committee (No. 20170131) and informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, W., Jiang, F., Chen, X. et al. Photoplethysmography-derived approximate entropy and sample entropy as measures of analgesia depth during propofol–remifentanil anesthesia. J Clin Monit Comput 35, 297–305 (2021). https://doi.org/10.1007/s10877-020-00470-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10877-020-00470-6

Keywords

Search

Navigation