麻醉深度监护系统的嵌入式设计

发布时间：2018-03-25 11:30

本文选题：麻醉深度监测　切入点：脑电信号　出处：《燕山大学》2012年硕士论文

【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。
[Abstract]:Depth monitoring of anesthesia is of great significance in guiding anesthetic use, reducing the risk of operation and patients suffering. Traditional monitoring methods are mainly based on the physiological parameters such as patient's spontaneous reaction and heart rate change, spontaneous epidermic myoelectric activity and so on. Based on the experience of anesthesiologists, it is estimated that there is a lack of clear quantitative indicators. In recent years, extensive attention has been paid to the depth monitoring of anesthesia based on electroencephalograms (EEGs) of scalp electroencephalograms. And there are many EEG anesthetic depth monitoring products. However, these monitoring products are mainly based on the linear system theory, the analysis of nonlinear EEG signal is defective, and the price is too expensive to be popularized in China. It is of great practical significance to explore a new depth monitoring algorithm and to develop an independent anesthetic depth monitor. This paper presents a scheme of anesthetic depth monitoring system based on digital signal processor digital signal processor (DSP), and analyzes the sources, characteristics and common de-noising schemes of various noises that affect the analysis of EEG signals. A denoising algorithm suitable for anesthetic depth monitoring and DSP calculation was proposed. The existing EEG signal analysis methods were discussed, and the algorithm based on sorting entropy was introduced in detail, including the principle of the algorithm and pharmacokinetics analysis. Statistical analysis and so on. The superior performance of sorting entropy is shown by comparison with other parameters. An algorithm for monitoring the depth of anesthesia based on multi-scale sorting entropy is also discussed. The pharmacokinetic analysis and pharmacokinetic analysis are carried out. Comparison of common anesthetic monitoring parameters, The results show that the method can also well reflect the EEG changes caused by the depth of anesthesia, and to some extent reveal the multi-scale characteristics of EEG signals. This paper introduces the design of front-end amplifier, channel switching circuit, data acquisition logic, data processing, data transmission and host receiving display. This paper introduces the program design and core algorithm realization of DSP platform, including the basic principle and realization of data acquisition and communication. The realization of correlation filtering algorithm and sorting entropy algorithm, and some key problems in programming and algorithm optimization are also discussed.
【学位授予单位】：燕山大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH772;R318.6

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