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研究生:蔡侃儒
研究生(外文):Kan-Ru Tsai
論文名稱:動態頻軸校正之自動喘息音偵測
論文名稱(外文):Automatic Wheeze Detection using Dynamic Frequency Warping
指導教授:吳國光吳國光引用關係
指導教授(外文):Kuo-Guau Wu
口試委員:蔡曉萍廖珗洲
口試日期:2011-07-21
學位類別:碩士
校院名稱:國立中興大學
系所名稱:電機工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:42
中文關鍵詞:動態頻軸校正喘息偵測
外文關鍵詞:Dynamic Frequency WarpingWheeze Detection
相關次數:
  • 被引用被引用:0
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  • 收藏至我的研究室書目清單書目收藏:0
肺音聽診是一個簡單且非侵入式的肺部診斷方法。由醫生藉聽診器來取得患者肺部的呼吸聲音,以判別是否有無異常或額外的聲音,依此診斷出肺部相關的疾病,如氣喘(Asthma)和慢性肺阻塞疾病(Chronic obstructive pulmonary disease,COPD)。然而,由醫生主觀且憑個人經驗來診斷肺部相關的疾病,可能會造成醫療診斷上的錯誤,因此對肺音訊號進行數位化處理以及適當地量化肺音,並根據分析結果來作判斷,將有助於醫生在診斷上更能精確地診斷其病因。
傳統常見的喘息音偵測之演算法,通常是根據喘息音在時域與頻域上的特性,如持續時間的長短(Duration)與出現的頻率範圍來判斷是否為喘息音。而這些用來判斷是否為喘息音的門檻值(Threshold)或準則(Rule)常藉著操作人的經驗法則設定,因此往往會受環境與雜訊等因素的影響,使得結果不如預期。
本研究提出結合動態頻軸校正之自動喘息音偵測的演算法,主要將喘息音位在不同頻帶上的能量調整至同一個頻帶上,以克服喘息音頻率變異所造成的影響。在本論文中,我們將利用子頻帶(Sub-Band)能量當作呼吸聲的特徵參數,再以動態頻軸校正(Dynamic Frequency Warping,DFW)調整呼吸聲之能量頻帶,並根據這些校正過的特徵參數來建立高斯模型(Gaussian Model),最後利用貝氏分類法(Bayesian Classification)來辨識其呼吸聲是否為喘息音。

Nowadays auscultation has been adopted by the physicians as easy, fast and noninvasive way to evaluate and diagnose patients with lung diseases, e.g. asthma (AS) and chronic obstructive pulmonary disease (COPD). Nevertheless, auscultation suffers from subjectivity and variability in the interpretation of its diagnostic information. In order to improve the quality of auscultation, automatic lung sound analysis employing digital signal processing techniques has attracted much attention recently. In this thesis, we will address the problem of automatic wheeze detection which is important for patients with AS. The main idea of the proposed algorithm is to employ the subband energy as the feature parameter and account for the frequency variation of the wheeze sound through dynamic frequency warping. Simulation results demonstrate that the proposed algorithm can achieve 100% wheeze detection for six lung sound databases.

目錄
摘要.....................................................................................................i
Abstract..............................................................................................ii
目錄...................................................................................................iii
圖目錄................................................................................................iv
表目錄.................................................................................................v
第一章緒論.................................................................................1
1.1 研究動機..........................................................................1
1.2 研究目的..........................................................................1
1.3 肺音的發生機制及其基本特性........................................2
第二章文獻回顧..........................................................................7
2.1 呼吸聲的種類以及相關特徵............................................7
2.2 喘息音偵測之相關方法.................................................10
2.3 研究方法與結果之比較.................................................19
第三章演算法原理與研究方法..................................................21
3.1 特徵擷取(Feature Extraction)......................................23
3.2 動態頻軸校正(Dynamic Frequency Warping)..............23
3.3 貝氏分類法(Bayesian Classification)............................27
3.3.1 貝氏定理.................................................................27
3.3.2 常態(高斯)分佈之貝氏分類法.................................28
第四章實驗結果與結論.............................................................29
4.1 實驗結果........................................................................32
4.2 討論...............................................................................37
4.3 結論...............................................................................40
4.4 未來展望........................................................................40
參考文獻............................................................................................41

圖目錄
圖1.1 氣喘時的細支氣管.................................................................5
圖2.4 喘息偵測之流程圖................................................................12
圖2.5 WED之流程圖......................................................................15
圖2.6 FDDT演算法之流程圖.........................................................17
圖2.7 演算法之流程圖....................................................................18
圖3.1 訓練端的流程圖....................................................................22
圖3.2 測試端之流程圖....................................................................23
圖3.3 比對平面...............................................................................25
圖3.4 比對平面上的最佳路徑........................................................26
圖4.1 音檔一之顯著的喘息音........................................................29
圖4.2 音檔二之顯著的喘息音........................................................30
圖4.3 音檔三之喘息音...................................................................31
圖4.4 音檔四之喘息音...................................................................31

表目錄
表1.1 正常呼吸聲種類的特徵以及命名..........................................3
表1.2 不正常呼吸音之特徵.............................................................4
表2.1 呼吸聲相關的特徵.................................................................8
表4.1 喘息音與正常呼吸音的偵測結果........................................33
表4.2 無動態頻軸校正之喘息音與正常呼吸聲的偵測結果..........34
表4.3 偵測結果..............................................................................34
表4.4 修正後之偵測結果...............................................................36
表4.5 偵測結果..............................................................................36
表4.6 SE與SP.................................................................................39
表4.7 不同子頻帶的模擬結果.......................................................39
表4.8 完整呼吸聲的SE與SP..........................................................40

中文部份
[1] 林靜幸, 張淑女, 周碧玲, 蘭菊梅, 徐惠禎, 陳瑞娥, 謝春滿, 陳翠芳, 李婉萍, 吳仙妮, 吳書雅, 方莉, 陳玉雲, 孫凡軻, 李業英, 蔡家梅, 曹英, 黃惠滿, “身體檢查與評估指引,” 藝軒, 2009
[2] 黃淑芬, “呼吸照護快速學習,” 合記, 2009
[3] 王小川, “語音訊號處理” 全華, 2005
西文部份
[4] Sandra Reichert, Raymond Gass, Christian Brandt, Emmanuel Andrès, “ Analysis of Respiratory Sounds: State of the Art,” Journal: Citation: Clinical Medicine: Circulatory, Respiratory and Pulmonary Medicine 2008:2 45-58.
[5] Mazic, J., Sovilj, S. and Magjarevic, R. “Analysis of respiratory sounds in asthmatic infants.” Polytechnic of Dubrovnik, Measurement Science Review, 3:11–21, 2003
[6] H. Pasterkamp, S. S. Kraman and G. R. Wodicka, “Respiratory Sounds Advances Beyond Stethoscope,” American Journal of Respiratory and Critical Care Medicine, vol. 156, pp. 974-987, 1997.
[7] S A Taplidou, and U Hadjileontiadis, “Wheeze detection based on timefrequency of breath sounds,” Computer in Biology and Medicine, vol. 37, pp. 1073-1083, 2007.
[8] A. Homs-Corbera, D. Salvatella, 1. A. Fiz, 1. Morera, and R. Jane, “Time-frequency characterization of wheezes during forced exhalation,” in Abstr. 5th Conf. Eur. Soc. Eng. Med., Barcelona, Spain, pp. 423 - 424, 1999.
[9] S.A. Taplidou, L.J. Hadjileontiadis, T. Penzel, V. Gross, S.M. Panas, “WED: an efficient wheezing-episode detector based on breath sounds spectrogram analysis,” in: Proceedings of the 25th International
43
Conference of the IEEE EMBS, Cancun, Mexico, 2003, pp. 2531–2534.
[10] Abhishek Banik, R.S. Anand and M.A. Ansari, “Remote Monitoring and Analysis of Human Lung Sound.” Industrial and Information Systems, IEEE Region 10 and the third international Conference on, 2008.
[11] J.C Chien, H.D Wu, F.C Chong, C.I Li, “Wheeze Detection using Cepstral Analysis in Gaussian Mixture Models.” Proceedings of the 29th Annual International Conference of the IEEE EMBS, August 23-26, 2007.
[12] J.E. Earis, A.R.A. Sovijarvi, J. Venderschoot, European respiratory society task force report: computerised respiratory sound analysis (CORSA): recommended standards for terms and techniques, Eur. Respir. Rev. 10 (2000) 585–649.

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