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

【摘要】：麻醉深度監(jiān)護對于指導麻醉用藥，減少手術風險和病人痛苦具有重要意義。傳統(tǒng)監(jiān)護方法主要基于病人的自主反應和心率變化、自發(fā)性表皮肌電等生理參數(shù)，靠麻醉師的經(jīng)驗來估計，缺乏清晰的量化指標。近年來，基于頭皮腦電信號(Electroencephalogram, EEG)的麻醉深度監(jiān)測技術得到了廣泛的重視，并有多款EEG麻醉深度監(jiān)護產(chǎn)品出現(xiàn)。然而這些監(jiān)護產(chǎn)品主要基于線性系統(tǒng)理論，分析非線性的EEG信號存在缺陷；而且價格昂貴，難以在國內推廣。因此，探討新的麻醉深度監(jiān)測算法，并據(jù)此研制有獨立技術的麻醉深度監(jiān)護儀具有重要的現(xiàn)實意義。 論文提出了一種基于數(shù)字信號處理器(Digital signal processor, DSP)的麻醉深度監(jiān)護系統(tǒng)方案。分析了影響腦電信號分析的各種噪聲的來源、特點和常見去噪方案，提出了適合麻醉深度監(jiān)護和DSP計算的去噪算法。探討了現(xiàn)有的腦電信號分析方法，并對基于排序熵的麻醉深度監(jiān)護算法進行了詳細介紹，包括算法原理、藥代藥效動力學分析、統(tǒng)計分析等。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)測算法。通過藥代藥效動力學分析和與另外幾種常見麻醉監(jiān)護參數(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的多尺度特性。 論文介紹了包括前端放大器的設計、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現(xiàn)。在此基礎上，，介紹了DSP平臺上的程序設計和核心的算法實現(xiàn)。包括數(shù)據(jù)采集、USB通信的基本原理和實現(xiàn)、相關濾波算法和排序熵算法的實現(xiàn)等。同時討論了程序設計和算法優(yōu)化中的一些關鍵問題。
[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.
【學位授予單位】：燕山大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH772;R318.6

【參考文獻】

相關博士學位論文 前1條

1 崔冬;多通道腦電信號建模及同步分析[D];燕山大學;2011年

相關碩士學位論文 前2條

1 王瑞婷;體外循環(huán)心臟手術中腦電雙頻指數(shù)變化影響因素分析[D];安徽醫(yī)科大學;2003年

2 于帥;腦電雙頻指數(shù)和熵指數(shù)監(jiān)測老年患者麻醉深度的比較研究[D];福建醫(yī)科大學;2009年

本文編號：1662927