麻醉深度监护系统的嵌入式设计
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摘要
麻醉深度监护对于指导麻醉用药，减少手术风险和病人痛苦具有重要意义。传统监护方法主要基于病人的自主反应和心率变化、自发性表皮肌电等生理参数，靠麻醉师的经验来估计，缺乏清晰的量化指标。近年来，基于头皮脑电信号(Electroencephalogram, EEG)的麻醉深度监测技术得到了广泛的重视，并有多款EEG麻醉深度监护产品出现。然而这些监护产品主要基于线性系统理论，分析非线性的EEG信号存在缺陷；而且价格昂贵，难以在国内推广。因此，探讨新的麻醉深度监测算法，并据此研制有独立技术的麻醉深度监护仪具有重要的现实意义。
论文提出了一种基于数字信号处理器(Digital signal processor, DSP)的麻醉深度监护系统方案。分析了影响脑电信号分析的各种噪声的来源、特点和常见去噪方案，提出了适合麻醉深度监护和DSP计算的去噪算法。探讨了现有的脑电信号分析方法，并对基于排序熵的麻醉深度监护算法进行了详细介绍，包括算法原理、药代药效动力学分析、统计分析等。通过与其他参数的比较显示了排序熵的优越性能。文中还探讨了一种基于多尺度排序熵的麻醉深度监测算法。通过药代药效动力学分析和与另外几种常见麻醉监护参数的比较，表明该方法也能够很好地反映麻醉深度造成的脑电变化，并在一定程度上揭示了脑电信号的多尺度特性。
论文介绍了包括前端放大器的设计、通道切换电路、数据采集逻辑、数据处理、数据传输及上位接收显示的完整硬件实现。在此基础上，介绍了DSP平台上的程序设计和核心的算法实现。包括数据采集、USB通信的基本原理和实现、相关滤波算法和排序熵算法的实现等。同时讨论了程序设计和算法优化中的一些关键问题。
Monitoring of the depth of anesthesia (DoA) is of great importance in guiding theapplication of anesthetics, avoiding sugery risks and reducing patients’pains. TraditionalDoA monitoring are generally based on patient’s reactions and physiological parameterssuch as heartrate variation and spontaneous skin myoelectricity. The judgement of DoAhighly relies on the experience of the anesthetist, and lacks a defined quantative measure.Great attention has been paid to DoA monitoring via electroencephalogram (EEG) analy-sis recently. A few products have emerged on the market. However, most products calcu-late their paramaters based on linear theory, therefore have deficits in analyzing non-linearEEG signals. The high price also restricts their application in China. Therefore, deveploingnew DoA monitoring algorithms and devices has a great significance.
A DoA monitor based on digital signal processor (DSP) is proposed in this disserta-tion. are discused. The source, characteristics and removal of artifacts within EEG are de-scribed, and de-noise solutions suitable for DoA monitoring and DSP realization are pro-posed. Some available EEG analysis methods are presented, and the DoA monitoring al-gorithm based on Permutation Entropy (PE) is described in detail, including the principleof PE, the pharmacokinetics/pharmacodynamics (PK/PD) analysis and statistical analysis.The comparision of PE with other indexes proved its advantage. Moreover, a DoA moni-toring algorithm based on the multiscale sample entropy is proposed. PK/PD analysisshows its validity in reflecting EEG changes with anesthetic concentration. The method isproved better than a few DoA paramaters. Paticularly, it reveals the multiscale characteris-tic of EEG signal to some extend.
The complete design of an EEG acquistition system is explained in this dissertation,including the front amplifier, channel muxing, acquistition logic, data processing, datatransmission and the data receive and display on the host system. Then the implement ofthe key algorithms on the DSP platform are explained. Main code for data acquistition,USB communication, digital filters and PE calculation are explained. Issues on program-ming and algorithm optimization are discussed.
论文提出了一种基于数字信号处理器(Digital signal processor, DSP)的麻醉深度监护系统方案。分析了影响脑电信号分析的各种噪声的来源、特点和常见去噪方案，提出了适合麻醉深度监护和DSP计算的去噪算法。探讨了现有的脑电信号分析方法，并对基于排序熵的麻醉深度监护算法进行了详细介绍，包括算法原理、药代药效动力学分析、统计分析等。通过与其他参数的比较显示了排序熵的优越性能。文中还探讨了一种基于多尺度排序熵的麻醉深度监测算法。通过药代药效动力学分析和与另外几种常见麻醉监护参数的比较，表明该方法也能够很好地反映麻醉深度造成的脑电变化，并在一定程度上揭示了脑电信号的多尺度特性。
论文介绍了包括前端放大器的设计、通道切换电路、数据采集逻辑、数据处理、数据传输及上位接收显示的完整硬件实现。在此基础上，介绍了DSP平台上的程序设计和核心的算法实现。包括数据采集、USB通信的基本原理和实现、相关滤波算法和排序熵算法的实现等。同时讨论了程序设计和算法优化中的一些关键问题。
Monitoring of the depth of anesthesia (DoA) is of great importance in guiding theapplication of anesthetics, avoiding sugery risks and reducing patients’pains. TraditionalDoA monitoring are generally based on patient’s reactions and physiological parameterssuch as heartrate variation and spontaneous skin myoelectricity. The judgement of DoAhighly relies on the experience of the anesthetist, and lacks a defined quantative measure.Great attention has been paid to DoA monitoring via electroencephalogram (EEG) analy-sis recently. A few products have emerged on the market. However, most products calcu-late their paramaters based on linear theory, therefore have deficits in analyzing non-linearEEG signals. The high price also restricts their application in China. Therefore, deveploingnew DoA monitoring algorithms and devices has a great significance.
A DoA monitor based on digital signal processor (DSP) is proposed in this disserta-tion. are discused. The source, characteristics and removal of artifacts within EEG are de-scribed, and de-noise solutions suitable for DoA monitoring and DSP realization are pro-posed. Some available EEG analysis methods are presented, and the DoA monitoring al-gorithm based on Permutation Entropy (PE) is described in detail, including the principleof PE, the pharmacokinetics/pharmacodynamics (PK/PD) analysis and statistical analysis.The comparision of PE with other indexes proved its advantage. Moreover, a DoA moni-toring algorithm based on the multiscale sample entropy is proposed. PK/PD analysisshows its validity in reflecting EEG changes with anesthetic concentration. The method isproved better than a few DoA paramaters. Paticularly, it reveals the multiscale characteris-tic of EEG signal to some extend.
The complete design of an EEG acquistition system is explained in this dissertation,including the front amplifier, channel muxing, acquistition logic, data processing, datatransmission and the data receive and display on the host system. Then the implement ofthe key algorithms on the DSP platform are explained. Main code for data acquistition,USB communication, digital filters and PE calculation are explained. Issues on program-ming and algorithm optimization are discussed.
引文
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[22] T.C. Ferree, P. Luu, G.S. Russell, et al. Scalp electrode impedance, infection risk, and EEG dataquality[J]. Clinical Neurophysiology, 2001, 112(3): 536-544.
[23] N.P. Castellanos, V.A. Makarov. Recovering EEG brain signals: artifact suppression with waveletenhanced independent component analysis[J]. Journal of Neuroscience Methods, 2006, 158(2):300-312.
[24] A. Lavric, N. Bregadze, A. Benattayallah. Detection of experimental ERP effects in combinedEEG-fMRI: evaluating the benefits of interleaved acquisition and independent componentanalysis[J]. Clinical Neurophysiology, 2011, 122(2): 267-277.
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[2] S. Sanei, J. Chambers. EEG signal processing[M]. England: Wiley-Interscience, 2007: 10-101.
[3]欧阳高翔.脑电非线性动力学分析：癫痫发作预测[D].秦皇岛:燕山大学工学博士论文, 2004:15-22.
[4]崔冬.多通道脑电信号建模及同步分析[D].秦皇岛:燕山大学工学博士论文, 2011: 8-16.
[5]于帅.脑电双频指数和熵指数监测老年患者麻醉深度的比较研究[D].福建:福建医科大学硕士学位论文, 2009: 1-4.
[6] I.J. Rampil. A primer for EEG signal processing in anesthesia[J]. Anesthesiology, 1998, 89(4):980-1002.
[7]王瑞婷.体外循环心脏手术中脑电双频指数变化影响因素分析[D].合肥:安徽医科大学硕士学位论文, 2003: 6-7.
[8] W.H. Kim, H.J. Ahn, J.A. Kim. Interactions of propofol and remifentanil on bispectral index under66% N(2)O: analysis by dose-effect curve, isobologram, and combination index[J]. Korean Journalof Anesthesiology, 2010, 59(6): 371-376.
[9] J.C. Diz, R. Del Rio, A. Lamas, et al. Analysis of pharmacodynamic interaction of sevoflurane andpropofol on Bispectral Index during general anaesthesia using a response surface model[J]. BritishJournal of Anaesthesia, 2010, 104(6): 733-739.
[10] P.A. March, W.W. Muir. Bispectral analysis of the electroencephalogram: a review of itsdevelopment and use in anesthesia[J]. Veterinary Anaesthesia and Analgesia, 2005, 32(5): 241-255.
[11] A.R. Absalom, G.N. Kenny. Closed-loop control of propofol anaesthesia using bispectral index:performance assessment in patients receiving computer-controlled propofol and manuallycontrolled remifentanil infusions for minor surgery[J]. British Journal of Anaesthesia, 2003, 90(6):737-741.
[12] O. Detsch, G. Schneider, E. Kochs, et al. Increasing isoflurane concentration may causeparadoxical increases in the EEG bispectral index in surgical patients[J]. British Journal ofAnaesthesia, 2000, 84(1): 33-37.
[13] T. Goto, Y. Nakata, H. Saito, et al. Bispectral analysis of the electroencephalogram does not predictresponsiveness to verbal command in patients emerging from xenon anaesthesia[J]. British Journalof Anaesthesia, 2000, 85(3): 359-363.
[14] O. Ortolani, A. Conti, B. Sall-Ka, et al. The recovery of Senegalese African blacks fromintravenous anesthesia with propofol and remifentanil is slower than that of Caucasians[J].Anesthesia and Analgesia, 2001, 93(5): 1222-1226.
[15] P.F. White, J. Tang, H. Ma, et al. Is the patient state analyzer with the PSArray2 a cost-effectivealternative to the bispectral index monitor during the perioperative period?[J]. Anesthesia andAnalgesia, 2004, 99(5): 1429-1435.
[16] H. Viertio-Oja, V. Maja, M. Sarkela, et al. Description of the Entropy algorithm as applied in theDatex-Ohmeda S/5 Entropy Module[J]. Acta anaesthesiologica Scandinavica, 2004, 48(2):154-161.
[17] S. Kreuer, A. Biedler, R. Larsen, et al. The Narcotrend--a new EEG monitor designed to measurethe depth of anaesthesia. A comparison with bispectral index monitoring duringpropofol-remifentanil-anaesthesia[J]. Anaesthesist, 2001, 50(12): 921-925.
[18] P. Panousis, A.R. Heller, M. Burghardt, et al. The effects of electromyographic activity on theaccuracy of the Narcotrend monitor compared with the Bispectral Index during combinedanaesthesia[J]. Anaesthesia, 2007, 62(9): 868-874.
[19] P. Myles, K. Leslie, J. Mcneil, et al. Bispectral index monitoring to prevent awareness duringanaesthesia: the B-Aware randomised controlled trial[J]. The Lancet, 2004, 363(9423): 1757-1763.
[20] T.J. Gan, P.S. Glass, A. Windsor, et al. Bispectral index monitoring allows faster emergence andimproved recovery from propofol, alfentanil, and nitrous oxide anesthesia[J]. Anesthesiology, 1997,87(4): 808.
[21] A. Vakkuri, A. Yli-Hankala, R. Sandin, et al. Spectral entropy monitoring is associated withreduced propofol use and faster emergence in propofol-nitrous oxide-alfentanil anesthesia[J].Anesthesiology, 2005, 103(2): 274.
[22] T.C. Ferree, P. Luu, G.S. Russell, et al. Scalp electrode impedance, infection risk, and EEG dataquality[J]. Clinical Neurophysiology, 2001, 112(3): 536-544.
[23] N.P. Castellanos, V.A. Makarov. Recovering EEG brain signals: artifact suppression with waveletenhanced independent component analysis[J]. Journal of Neuroscience Methods, 2006, 158(2):300-312.
[24] A. Lavric, N. Bregadze, A. Benattayallah. Detection of experimental ERP effects in combinedEEG-fMRI: evaluating the benefits of interleaved acquisition and independent componentanalysis[J]. Clinical Neurophysiology, 2011, 122(2): 267-277.
[25] J. Mcclellan, T. Parks. A united approach to the design of optimum FIR linear-phase digitalfilters[J]. IEEE Transactions on Circuit Theory, 1973, 20(6): 697-701.
[26] B. Widrow, J.R. Glover Jr, J.M. Mccool, et al. Adaptive noise cancelling: Principles andapplications[J]. Proceedings of the IEEE, 1975, 63(12): 1692-1716.
[27] A.A. Dingle, R.D. Jones, G.J. Carroll, et al. A multistage system to detect epileptiform activity inthe EEG[J]. IEEE Transactions on Bio-medical Engineering, 1993, 40(12): 1260-1268.
[28] C. Tallon-Baudry, O. Bertrand, C. Fischer. Oscillatory synchrony between human extrastriate areasduring visual short-term memory maintenance[J]. The Journal of Neuroscience, 2001, 21(20): 177.
[29] L. Senhadji, J.L. Dillenseger, F. Wendling, et al. Wavelet analysis of EEG for three-dimensionalmapping of epileptic events[J]. Annals of Biomedical Engineering, 1995, 23(5): 543-552.
[30] X. Li, M. Chu, T. Qiu, et al. An EMD based time-frequency distribution and its application in EEGanalysis[J]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 2007, 24(5): 990-995.
[31] R. Shalbaf, H. Behnam, J.W. Sleigh, et al. Using the Hilbert-Huang transform to measure theelectroencephalographic effect of propofol[J]. Physiological Measurement, 2012, 33(2): 271-285.
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