Journal of the Korean Institute of Electrical and Electronic Material Engineers (한국전기전자재료학회논문지)

The Korean Institute of Electrical and Electronic Material Engineers (한국전기전자재료학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation on the Depth of Anesthesia using Linear and Nonlinear Analysis of HRV

HRV 신호의 선형 및 비선형 분석을 이용한 마취심도 평가

  • 예수영 (부산대학교 BK21 고급의료인력양성사업단) ;
  • 백승완 (부산대학교 마취통증의학교실) ;
  • 김혜진 (부산대학교 마취통증의학교실) ;
  • 김태균 (부산대학교 마취통증의학교실) ;
  • 전계록 (부산대학교 의공학교실)
  • Published : 2010.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

In general, anesthetic depth is evaluated by experience of anesthesiologist based on the changes of blood pressure and pulse rate. So it is difficult to guarantee the accuracy in evaluation of anesthetic depth. The efforts to develop the objective index for evaluation of anesthetic depth were continued but there was few progression in this area. Heart rate variability provides much information of autonomic activity of cardiovascular system and almost all anesthetics depress the autonomic activity. Novel monitoring system which can simply and exactly analyze the autonomic activity of cardiovascular system will provide important information for evaluation of anesthetic depth. We investigated the anesthetic depth as following 7 stages. These are pre-anesthesia, induction, skin incision, before extubation, after extubation, Post-anesthesia. In this study, temporal, frequency and chaos analysis method were used to analyze the HRV time series from electrocardiogram signal. There were NN10-NN50, mean, SDNN and RMS parameter in the temporal method. In the frequency method, there are LF and HF and LF/HF ratio, 1/f noise, alphal and alpha2 of DFA analysis parameter. In the chaos analysis, there are CD, entropy and LPE. Chaos analysis method was valuable to estimate the anesthetic depth compared with temporal and frequency method. Because human body was involved the choastic character.

Keywords

References

  1. Jeleazcov C., Fechner J., and Schwilden H., "Electroencephalogram monitoring during anesthesia with propofol and alfentanil: the impact of second order spectral analysis", Anesth. Analg., Vol. 100, p. 1365, 2005. https://doi.org/10.1213/01.ANE.0000148689.35951.BA
  2. Z. Feng and X. Zheng, “Changes in complexities and power spectrum of rat electroencephalogram under various anesthesia depth”, Engineering in Medicine and Biology, Vol. 1, p. 145, 2002.
  3. S. W. Baik, T. K. Kim, J. H. Kim, G. R. Jeon, and S. Y. Ye, “Comparison of heart rate variability with pulse transit time", J. of KIEEME(in Korean), Vol. 22, No. 6, p. 537, 2009.
  4. Hanss R., Bein B., Ledowski T., Lehmkuhl M., Ohnesorge H., Scherkl W., Steinfath M., Scholz J., and Tonner P. H., “Heart rate variability predicts severe hypotension after spinal anesthesia”, Anesthesiology, Vol. 104, p. 537, 2006. https://doi.org/10.1097/00000542-200603000-00022
  5. Park S. K., Kang S. J., Im H. S., Cheon M. Y., Bang J. Y., Shin W. J., Choi B. M., Youn M. O., Kim Y. K., Hwang G. S., and Cho S. K., "Validity of heart rate variability using Poincare plot for assessing vagal tone during general anesthesia", Korean J. Anesthesiol, Vol. 49, p. 765, 2005. https://doi.org/10.4097/kjae.2005.49.6.765
  6. Signorini, M. G., Marchetti, F., and Cerutti, S., "Applying nonlinear noise reduction in the analysis of heart rate variability", IEEE Engineering in Medicine and Biology Magazine, Vol. 20, Issue 2, p. 59, 2001. https://doi.org/10.1109/51.917725
  7. Casolo G. C., Stroder P., Signorini C., Calzolari F., Zucchini M., Balli E., Sulla A., and Lazzerini S., "Heart rate variability during the acute phase of myocardial infarction", Cir., Vol. 85, p. 2073, 1992. https://doi.org/10.1161/01.CIR.85.6.2073
  8. C. M. Dougherty and R L. Burr, "Comparison of heart rate variability in survivors and nonsurvivors of sudden cardiac arrest", Am. J. Cardiol., Vol. 70, p. 441, 1992. https://doi.org/10.1016/0002-9149(92)91187-9
  9. B. Pomeranz, R. J. Macaulay, M. A. Caudill, I. Kutz, D. Adam, D. Gordon, K. M. Kilborn, A. C. Barger, D. C. Shannon, and R. J. Cohen, "Assessment of autonomic function in humans by heart rate spectral analysis", Am. J. Physiol., Vol. 249, p. H151, 1985.
  10. Yoki M., Morita Y., Kimura T., Doya M., and Kaneda T., "Effects of trend and term sampling on power spectral analysis of heart rate variability during tracheal intubation", Anesthesiology, Vol. 85(3A), p. A406, 1996.
  11. F. N. Hooge, "1/f noise sources", IEEE Transactions on Electron Devices, Vol. 41, No. 11. p. 1926, 1994. https://doi.org/10.1109/16.333808
  12. Timo T. Laitio, Heikki V. Huikuri, Timo H. Ma¨kikallio, J. Jalonen, Erkki S. H. Kentala, H. Helenius, O. Pullisaar, J. Hartiala, and H. Scheinin, "The breakdown of fractal heart rate dynamics predicts prolonged postoperative myocardial ischemia", Anesth. Analg., Vol. 98, p. 1239, 2004.
  13. Peng C. K., Havlin S., Stanley H. E., and Goldberger A. L., “Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series”, Chaos, Vol. 5, p. 82, 1995. https://doi.org/10.1063/1.166141
  14. Goldberger A. L., “Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside”, Lancet, Vol. 347, p. 1312, 1996. https://doi.org/10.1016/S0140-6736(96)90948-4
  15. A. M. Fraser and H. L. Swinney, “Independent coordinates for strange attractors from mutual information”, Phys. Rev. A, Vol. 33, p. 1134, 1986. https://doi.org/10.1103/PhysRevA.33.1134
  16. Kennel M. B., Brown R., and Abarbanel H. D. I., “Determining embedding dimension for phasespace reconstruction using a geometrical construction”, Phys. Rev. A, Vol. 45, p. 3403, 1992. https://doi.org/10.1103/PhysRevA.45.3403
  17. Sano M. and Sawada Y., "Measurement of the Lyapunov spectrum from a chaotic time series", Phys. Rev. Lett., Vol. 55, p. 1082, 1985. https://doi.org/10.1103/PhysRevLett.55.1082
  18. Kennel, Matthew B., R. Brown, and H. D. I. Abarbanel, “Determining embedding dimension for phase-space reconstruction using a geometrical construction”, Phy. Rev. A, Vol. 45, p. 3403, 1992. https://doi.org/10.1103/PhysRevA.45.3403
  19. W. J. Freeman, “Tutorial in neurobiology: From single neurons to brain chaos”, International Journal of Bifurcation and Chaos, Vol. 2, p. 451, 1992. https://doi.org/10.1142/S0218127492000653
  20. Ivanov D. K., Posch H. A., and Stumpf C., “Statistical measures derived from the correlation integrals of physiological time series”, Chaos, Vol. 6, No. 2, p. 243, 1996. https://doi.org/10.1063/1.166170
  21. Rain F., Tarmo L., Andres A., Ville J., Sari M., and Seppo H., "Comparison of entropy and complexity measures for the assessment of depth of sedation", IEEE Transactions on Biomedical Engineering, Vol. 53, No. 6, p. 1067, 2006. https://doi.org/10.1109/TBME.2006.873543