Signal averaging for noise reduction in anesthesia monitoring and control with communication channels
ABSTRACT
This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local area networks, or tele-medicine over a wide area network. Such systems involve communication channels which introduce noises due to quantization, channel noises, and have limited communication bandwidth resources. Usually signal averaging can be used effectively in reducing noise effects when remote monitoring and diagnosis are involved. However, when feedback is intended, we show that signal averaging will lose its utility substantially. To explain this phenomenon, we analyze stability margins under signal averaging and derive some optimal strategies for selecting window sizes. A typical case of anesthe-sia depth control problems is used in this development.
This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local area networks, or tele-medicine over a wide area network. Such systems involve communication channels which introduce noises due to quantization, channel noises, and have limited communication bandwidth resources. Usually signal averaging can be used effectively in reducing noise effects when remote monitoring and diagnosis are involved. However, when feedback is intended, we show that signal averaging will lose its utility substantially. To explain this phenomenon, we analyze stability margins under signal averaging and derive some optimal strategies for selecting window sizes. A typical case of anesthe-sia depth control problems is used in this development.
Cite this paper
nullTan, Z. , Wang, L. and Wang, H. (2009) Signal averaging for noise reduction in anesthesia monitoring and control with communication channels. Journal of Biomedical Science and Engineering, 2, 564-573. doi: 10.4236/jbise.2009.27082.
nullTan, Z. , Wang, L. and Wang, H. (2009) Signal averaging for noise reduction in anesthesia monitoring and control with communication channels. Journal of Biomedical Science and Engineering, 2, 564-573. doi: 10.4236/jbise.2009.27082.
References
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[1] Nunes, C. S., Mendonca, T., Lemos, J. M., and Amorim, P., (2007) Predictive adaptive control of the bispectral index of the EEG (BIS): Exploring electromyography as an accessible dis-turbance, Mediterranean Conference on Control and Automa-tion, Athens-Greece. [2] Gentilini, A., Rossoni-Gerosa, M., and Morari, M., et al, (2001) Modeling and closed-loop control of hypnosis by means of bispectral index (BIS) with isoflurane, IEEE Trans. on Bio-medical Engineering, 48, 874?889,. [3] Dong, C., Kehoe, J., Henry, J., Ifeachor, E. C., Reeve, C. D., and Sneyd, J. R., (1999) Closed-loop computer controlled se-dation with propofol, Proc. of the Anaesthetic Research Soci-ety, 631. [4] Zhang, X. S., Roy, R. J., and Huang, J. W., (1998) Closed- loop system for total intravenous anesthesia by simultaneously ad-ministering two anesthetic drugs, Proc. of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology, 3052–3055. [5] Goldman, J. M., (2006) Medical device connectivity for im-proving safety and efficiency, American Society of Anesthesi-ologists Newsletters, 70(5), http://www.asahq.org/Newsletters/2006/05?06/goldman05_06.html. [6] Talbot, S. L. and Boroujeny, B. F., (2008) Spectral method of blind carrier tracking for OFDM, IEEE Transactions on Signal Processing, 56(7). [7] Bataillou, E., Thierry, E., Rix, H., and Meste, O., (1995) Weighted averaging using adaptive estimation of the weights, Signal Processing, 44, 5l?66. [8] Eisenach, J. C., (1999) Reports of Scientific Meetings— Workshop on Safe Feedback Control of Anesthetic Drug De-livery, Anesthesilogy, 91, 600–601. [9] Linkens, D. A., (1992) Adaptive and intelligent control in an-esthesia, IEEE Control Systems Magazine, 6–11. [10] Wang, L. Y. and Wang, H., (2002) Control-oriented modeling of BIS-based patient response to anesthesia infusion, Internat. Conf. Math. Eng. Techniques in Medicine and Bio. Sci., Las Vegas. [11] Wang, L. Y. and Wang, H., (2002) Feedback and predictive control of anesthesia infusion using control-oriented patient models, Internat. Conf. Math. Eng. Techniques in Medicine and Bio. Sci., Las Vegas. [12] Wang, L. Y., Wang, H., and Yin, G., (2002) Anesthesia infusion models: Knowledge-based real-time identification via stochas-tic approximation, 41st IEEE Cont. and Dec. Conf., Las Vegas. [13] Gan, T. J., et al., (1997) Bispectral index monitoring allows faster emergence and improved recovery from propofol, Alfentanil, and Nitrous Oxide Anesthesia, Anesthesiology, 87, 808–815. [14] Rosow, C. and Manberg, P. J., (1998) Bispectral index moni-toring, Annual of Anesthetic Pharmacology, 2, 1084– 2098. [15] Ljung, L. and S?derstr?m, T., (1983) Theory and Practice of Recursive Identification, MIT Press, Cambridge, MA