本文選題：麻醉深度監(jiān)測(cè)　切入點(diǎn)：腦電信號(hào)　出處：《燕山大學(xué)》2012年碩士論文

【摘要】：麻醉深度監(jiān)護(hù)對(duì)于指導(dǎo)麻醉用藥，減少手術(shù)風(fēng)險(xiǎn)和病人痛苦具有重要意義。傳統(tǒng)監(jiān)護(hù)方法主要基于病人的自主反應(yīng)和心率變化、自發(fā)性表皮肌電等生理參數(shù)，靠麻醉師的經(jīng)驗(yàn)來估計(jì)，缺乏清晰的量化指標(biāo)。近年來，基于頭皮腦電信號(hào)(Electroencephalogram, EEG)的麻醉深度監(jiān)測(cè)技術(shù)得到了廣泛的重視，并有多款EEG麻醉深度監(jiān)護(hù)產(chǎn)品出現(xiàn)。然而這些監(jiān)護(hù)產(chǎn)品主要基于線性系統(tǒng)理論，分析非線性的EEG信號(hào)存在缺陷；而且價(jià)格昂貴，難以在國內(nèi)推廣。因此，探討新的麻醉深度監(jiān)測(cè)算法，并據(jù)此研制有獨(dú)立技術(shù)的麻醉深度監(jiān)護(hù)儀具有重要的現(xiàn)實(shí)意義。 論文提出了一種基于數(shù)字信號(hào)處理器(Digital signal processor, DSP)的麻醉深度監(jiān)護(hù)系統(tǒng)方案。分析了影響腦電信號(hào)分析的各種噪聲的來源、特點(diǎn)和常見去噪方案，提出了適合麻醉深度監(jiān)護(hù)和DSP計(jì)算的去噪算法。探討了現(xiàn)有的腦電信號(hào)分析方法，并對(duì)基于排序熵的麻醉深度監(jiān)護(hù)算法進(jìn)行了詳細(xì)介紹，包括算法原理、藥代藥效動(dòng)力學(xué)分析、統(tǒng)計(jì)分析等。通過與其他參數(shù)的比較顯示了排序熵的優(yōu)越性能。文中還探討了一種基于多尺度排序熵的麻醉深度監(jiān)測(cè)算法。通過藥代藥效動(dòng)力學(xué)分析和與另外幾種常見麻醉監(jiān)護(hù)參數(shù)的比較，表明該方法也能夠很好地反映麻醉深度造成的腦電變化，并在一定程度上揭示了腦電信號(hào)的多尺度特性。 論文介紹了包括前端放大器的設(shè)計(jì)、通道切換電路、數(shù)據(jù)采集邏輯、數(shù)據(jù)處理、數(shù)據(jù)傳輸及上位接收顯示的完整硬件實(shí)現(xiàn)。在此基礎(chǔ)上，，介紹了DSP平臺(tái)上的程序設(shè)計(jì)和核心的算法實(shí)現(xiàn)。包括數(shù)據(jù)采集、USB通信的基本原理和實(shí)現(xiàn)、相關(guān)濾波算法和排序熵算法的實(shí)現(xiàn)等。同時(shí)討論了程序設(shè)計(jì)和算法優(yōu)化中的一些關(guān)鍵問題。
[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.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH772;R318.6

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本文編號(hào)：1662927