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研究生： 陳威豪
Chen, Wei-Hao
論文名稱： 設計及發展一套線上聲學導引氣管插管輔助系統於聲門口偵測
Design and Development of an On-Line Acoustic-Guided Endotracheal Intubation Assistive System for Glottic Opening Detection
指導教授： 鄭國順
Cheng, Kuo-Sheng
學位類別： 博士
Doctor
系所名稱： 工學院 - 生物醫學工程學系
Department of BioMedical Engineering
論文出版年： 2015
畢業學年度： 103
語文別： 英文
論文頁數： 79
中文關鍵詞： 氣管插管 、高斯混合模型 、梅爾倒頻譜係數 、差分貝氏資訊法則 、聲學識別
外文關鍵詞： Endotracheal intubation, Gaussian mixture model, mel frequency ceptstral coefficients, delta Bayesian information criterion, acoustic recognition
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    • 針對呼吸衰竭或是重症病患的呼吸道處理，如何在最少的錯誤嘗試下、快速且成功地完成氣管插管是不可或缺的一環。重複的插管嘗試不僅容易造成呼吸道傷害，還會增加胃內容物嘔吐與吸入肺部的機會、甚至因血行動力學不穩定而導致死亡。目前氣管插管只有68 %的病患在第一次插管嘗試就成功；有10 %以上的病患需要三次或以上的嘗試才能成功置入氣管內管。因此發展一套線上判斷聲門口位置的氣管插管輔助系統，將能大幅減少不必要的插管嘗試與相關併發症的發生。
    本研究目的為透過聲音訊號處理技術，設計與發展氣管插管輔助系統，可在插管過程中自動識別非聲門與聲門氣流音，進而協助偵測聲門邊界與開口。特定目標主要為：1) 發展聲學導引氣管插管輔助系統 ：包括下咽吹氣模組、電子聽診器、以及聲音訊號處理之軟硬體；2) 系統校準與測試，藉由解析下咽處不同構造在吹氣後所產生之氣流聲，擷取出可供區辨之訊號特徵；3) 發展線上聲學導引聲門口偵測訊號處理流程，透過插管病患臨床實驗來驗證所提系統之可行性。第一組實驗使用線性預測係數與梅爾倒頻譜係數作為聲音特徵參數，分別評估其在線性判別分析模型與統計式高斯混合模型之聲門與非聲門氣流音的識別正確性；第二組實驗應用所提之基於高斯混合模型為基礎的概似比演算法，區辨插管操作者動態尋找聲門過程中氣流音的變化，並偵測非聲門轉成聲門的聲學預估邊界位置；第三組實驗進一步應用所發展的兩階段連續聲音段落聲學分析法來進行線上聲門識別，透過基於高斯混合模型之差分貝氏資訊法則、以及多票決選機制之兩階段處理來輔助偵測聲門口。
    第一組實驗結果顯示：基於12階梅爾倒頻譜係數之高斯混合模型進行聲學分類，當混合模型數目為3時，分類正確率可達92.34%；第二組實驗結果顯示：應用所提的高斯混合模型為基礎的概似比演算法，通過真正聲門邊界且被偵測出的正確率為100%，而聲學預估邊界與真正聲門邊界對應時間差距小於416 msec；第三組實驗結果顯示：應用所發展的兩階段連續聲音段落聲學分析法於線上聲門識別，通過真正聲門邊界且被偵測出的正確率為77.78%，聲學預估邊界與真正聲門邊界對應的時間差距小於384 msec。從研究結果顯示本研究發展的下咽氣流音訊號分析流程可輔助操作者快速並正確偵測聲門口與判斷聲門位置。未來可應用本研究成果開發臨床氣管插管聲門口偵測輔助系統，增加第一次氣管插管成功率、減少不必要的重複插管過程並改善病患安全。

    Accomplishing successful endotracheal intubation (ETI) both rapidly and with a minimum number of attempts is a crucial skill for clinicians in order to manage patient airways and acute care. Repeated attempts may cause airway injury and increase the occurrence of hypoxemia, regurgitation or aspiration of gastric contents, hemodynamic instability and even death. Successful ETI within minimum attempts is highly dependent on clinician experience. Most current devices help confirm endotracheal tube position after intubation, but are not useful in reducing unnecessary attempts. It would be highly beneficial to develop an objective glottis identification method during ETI to minimize unnecessary intubation attempts.
    The purpose of this research was to develop an innovative acoustic-guided tracheal intubation assistive system to help ETI providers judge among the glottis and other hypopharyngeal structures during the procedure. By insufflating oxygen into hypopharynx, the acoustic responses of non-glottic structures and the glottis were analyzed by the proposed algorithm to recognize the glottis and determine the glottic boundary. More specifically, the research describes 1) development of a PC-based acoustic-guided tracheal intubation assistive system, including oxygen insufflation device, electronic stethoscope system, and laptop with acoustic signal analysis software; 2) system calibration and analysis in response to oxygen insufflation of hypopharyngeal structures; 3) development of an on-line acoustic-guided glottis identification algorithm to investigate the feasibility in situ using live ETI cases. In the first study, the feasibility of acoustic analysis for glottis discrimination was investigated by using linear prediction coefficients (LPCs) and mel frequency ceptstral coefficients (MFCCs) as the representative sound features in linear discriminant analysis (LDA) and Gaussian mixture model (GMM) methods. In the second study, to simulate the glottis searching process during ETI, a GMM-based likelihood ratio algorithm was developed to determine the acoustic evaluated boundary between non-glottic and glottic segments in the sound recordings. In the third study, in order to accomplish on-line glottis recognition, a two-stage segment-based acoustic approach including GMM-based delta Bayesian information criterion (delta-BIC) and majority vote was applied for fast glottis identification.
    The results of the first study showed the proposed GMM-based classifier outperformed the conventional LDA method in accuracy (92.34% vs. 86.35%) at the mixture number of 3. The results of the second study showed the actual boundaries in all 9 cases were successfully detected by the GMM-based likelihood ratio method. The time differences between the evaluated boundary and actual boundary were less than 416 msec. The result of the third study using the proposed two-stage segmented-based acoustic approach showed the success rate of glottic boundary recognition was 77.78% for a single screening attempt. The time differences between the evaluated and actual boundary were less than 384 msec. This series of studies demonstrate the acoustic-guided tracheal intubation assistive system developed in this work is likely to be feasible for on-line glottis and glottic boundary detection during ETI. The eventual goal is to develop an innovative objective monitor to increase initial endotracheal intubation success rate, minimize unnecessary intubation attempts and increase patient safety for airway management.

    中文摘要...............................................................................................................I ABSTRACT..........................................................................................................III 誌謝...................................................................................................................VI CONTENTS........................................................................................................VII LIST OF TABLES..................................................................................................IX LIST OF FIGURES.................................................................................................X CHAPTER 1 INTRODUCTION................................................................................1 1.1. Background and Literatures Review..............................................................4 1.1.1. Anatomy of upper airway..........................................................................6 1.1.2. Endotracheal intubation............................................................................8 1.1.3. Tracheal tube position confirmation........................................................12 1.1.4. Oxygen insufflation in endotracheal intubation.......................................16 1.2. Motivation.................................................................................................17 1.2.1. Purpose and specific aims.......................................................................18 1.2.2. Research hypothesis...............................................................................18 1.2.3. Significance............................................................................................19 1.3. Organization of Dissertation......................................................................20 CHAPTER 2 MATERIALS AND METHODS.............................................................21 2.1. System Design and Development...............................................................22 2.1.1. An acoustic-guided endotracheal intubation assistive system..................23 2.1.2. Software development for sound signal processing.................................25 2.2. Acoustic-Guided Approach to Detect the Glottis........................................30 2.2.1. Subjects, sound acquisition and preprocessing........................................30 2.2.2. Glottis discrimination using acoustic features..........................................32 2.2.3. Glottic boundary detection using GMM-based likelihood ratio method....39 2.2.4. Fast glottis identification using two-stage segment-based acoustic approach..........................................................................................................44 CHAPTER 3 EXPERIMENTAL RESULTS AND DISCUSSION.......................................50 3.1. The Prototype System and Mannequin Simulation......................................50 3.2. Results on Discriminative Acoustic Features...............................................56 3.3. Results on Glottic Boundary Detection........................................................62 3.4. Results on Fast Glottic Identification..........................................................66 3.5. Summary...................................................................................................70 CHAPTER 4 CONCLUSIONS AND FUTURE STUDY................................................71 4.1. Conclusions...............................................................................................71 4.2. Future Works.............................................................................................72 REFERENCES.....................................................................................................75

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