以希爾伯特-黃轉換抑制肺音中心音干擾

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：9

、訪客IP：3.144.233.150

姓名

許時挺(Shr-ting Shiu) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

以希爾伯特-黃轉換抑制肺音中心音干擾
(Reducing heart sound interference from lung sounds by Hilbert-Huang transform)

相關論文

★ 電子式基因序列偵測晶片之原型	★ 眼動符號表達系統之可行性研究
★ 利用網印碳電極以交流阻抗法檢測糖化血紅素	★ 電子式基因序列偵測晶片可行性之研究
★ 電腦化肺音擷取系統	★ 眼寫鍵盤和眼寫滑鼠
★ 眼寫電話控制系統	★ 氣喘肺音監測系統之可行性研究
★ 肺音聽診系統之可行性研究	★ 穿戴式腳趾彎曲角度感測裝置之可行性研究
★ 注音符號眼寫系統之可行性研究	★ 英文字母眼寫系統之可行性研究
★ 數位聽診器之原型	★ 使用角度變化率為基準之心電訊號壓縮法
★ 電子式基因微陣列晶片與應用電路研究	★ 電子聽診系統應用於左右肺部比較之臨床研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

對於一般肺部疾病，醫師最先採取的診療方式，就是使用聽診器是來聽取肺音訊號。但由於器材與環境的限制，肺音常受到雜訊的干擾。其中，心音是主要的干擾來源。由於肺音與心音並不是線性非時變(Linear and Time-Invariant, LTI)且不是平穩(Stationary)訊號，所以若使用傳統的傅立葉轉換(Fourier Transform)來進行分析，將會無法獲得正確的資訊。本論文使用黃鍔博士在1988年所提出的希爾伯特-黃轉換(Hilbert-Huang Transform)理論，這種轉換特別適合用來處理非平穩的訊號。透過經驗模態分解(EMD)，將訊號分解為一組內蘊模式函數(IMF)；內蘊模式函數通常由高頻至低頻被分離出來。接著，對每一個內蘊模式函數分量進行希爾伯特轉換(Hilbert Transform)，得到頻率對時間的瞬時變化。希爾伯特頻譜(Hilbert Spectrum)在時域以及頻域具有良好的分辨率，故在此使用希爾伯特-黃轉換，來分析心音與肺音訊號，藉以降低心音對肺音的干擾；幫助醫療人員在診斷上，有更好的判斷與觀察。

摘要(英)

In this research, we take heart sound signals as interference to lung sounds and propose a method to reduce the interfering heart sounds in lung sounds. The lung sounds were obtained by placing an electronic stethoscope head on the chest of the subject and recording the output signal of the microphone in the stethoscope head. We incorporated Hilbert-Huang Transform (HHT) in our heart sound reduction. HHT was proposed by Norden E. Huang. It is especially suitable for processing non-stationary and non-linear signals, such as physiological signals. In HHT, the target signal can be decomposed into a number of intrinsic mode functions (IMFs) by empirical mode decomposition (EMD).These IMFs can be transformed into the Hilbert space, and then their instantaneous frequencies can be observed in the time domain. The performance of our heart sound reduction algorithm was evaluated in terms of the heart-sound-noise reduction percentage (HNRP), which .is about 80% in our experiments. This result is comparatively better than that of a wavelet-based method shown in the literature.

關鍵字(中)

★ 驗模態分解
★ 肺音
★ 希爾伯特轉換
★ 內蘊模式函數
★ 心音

關鍵字(英)

★ HHT
★ heart sounds
★ lung sounds
★ EMD
★ IMF

論文目次

中文摘要 I
英文摘要 II
目錄 IV
圖目錄 VI
表目錄 XIII
第一章緒論
1-1 研究動機 1
1-2 肺音概述 2
1-3 心音概述 4
1-4文獻回顧 5
第二章方法與原理
2-1 希爾伯特-黃轉換介紹 7
2-2 短時距傅立葉轉換介紹 40
2-3 連續小波轉換介紹 42
第三章實際肺音訊號測試
3-1 肺音訊號擷取電路 48
3-2 EMD方法測試 59
3-3 EEMD方法測試 68
3-4 小波方法測試 75
第四章實驗結果與討論
4-1 顯著性檢定 81
4-2 時頻譜分析 87
4-3 Power Spectrum Density 89
4-4 HNRP 95
第五章結論與未來展望
5-1 結論 97
5-2 未來展望 97
參考文獻 98

參考文獻

[1] Norden E. Huang, Samuel S.P. Shen, “ Hilbert-Huang transform and Its applications ”, New Jersey , World Scientific, 2005.
[2] Richard K. Albert, Stephen G. Spiro and James R. Jett, “Clinical Respiratory Medicine”, Elsevier Inc, 2008.
[3] Leontios Hadjileontiadis , “Lung Sounds: An Advanced Signal Processing Perspective”, Morgan and Claypool Publishers, 2008.
[4] Zahra Moussavi, “Fundamentals of Respiratory System and Sounds Analysis”, Morgan and Claypool Publishers, 2006.
[5] McKusick VA, Jenkins JT, Webb GN. “The acousticbasis of the chest examination: studies by means of soundspectrography”, Am Rev Tuberc 1955; 72: 12–34.
[6] E. P. Widmaier, “Human Physiology：the mechanisms of body Function”, 9th edition, McGraw-Hill, 2003.
[7] Barbara Erickson, PhD, RN, CCRN, “Heart Sound And Murmurs”Across the lifespan, Fourth Edition, 2003.
[8] Leontios J. Hadjileontiadis and Stavros M. Panas, “Adaptive Reduction of Heart Sounds from Lung SoundsUsing Fourth-Order Statistics”, IEEE Transactions On Biomedical Engineering, Vol. 44, No. 7, July 1997.
[9] Sonia Charleston, Mahmood R. Azimi-Sadjadi, and Ramon Gonz’alez-Camarena, “ Interference Cancellation in Respiratory Sounds via a Multiresolution Joint Time-Delayand Signal-Estimation Scheme ”, IEEE Transactions on Biomedical Engineering, Vol. 44, No. 10, October 1997.
[10] Thato Tsalaile and Saeid Sanei, “Separation Of Heart Sound Signal From Lung Sound Signal Byadaptive Line Enhancement ”, 15th European Signal Processing Conference (EUSIPCO 2007), Poznan, Poland, September 3-7, 2007.
[11] M.T.Pourazad, Z.Moussavi, F.Farahmand, R.K.Ward, “Heart Sounds Separation From Lung Sounds Using Independent Component Analysis”, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005.
[12] Azadeh Yadollahi and Zahra M. K. Moussavi, “A Robust Method for Heart Sounds LocalizationUsing Lung Sounds Entropy”, IEEE Transactions On Biomedical Engineering, Vol. 53, No. 3, March 2006.
[13] Leontios J. Hadjileontiadis and Stavros M. Panas, “A wavelet-based reduction of heart sound noise from lung sounds”, International Journal of Medical Informatics 52 (1998) 183–190.
[14] Ioannis T. Rekanos and Leontios J. Hadjileontiadis, “An iterative kurtosis-based technique for the detection of nonstationary bioacoustic signals”, Signal Processing 86 (2006) 3787–3795.
[15] 吳順德,陳思予,陳虹伯“經驗模態分解法之研究趨勢探討與問題分析”,臺北科技大學學報, No.42-1, 2009.
[16] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung and H. H. Liu, “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non- stationary Time Series Analysis”, Proc. R. Soc. Lond. A, vol. 454, 1998, pp. 903- 995.
[17] N. E. Huang, M. C. Wu, S. R. Long, S. S. P. Shen, W. Qu, P. Gloersen and K. L. Fan, “A Confidence Limit for the Empirical Mode Decomposition and Hilbert Spectrum Analysis”, Proc. R. Soc. Lond. A, vol. 459, 2003, pp. 2317- 2345.
[18] G. Rilling, P. Flandrin and P. Goncalves, “On Empirical Mode Decomposition and Its Algorithms”, IEEE-EURASIP Work- shop on Nonlinear Signal and Image Processing NSIP-03, Grado, Italy, 8-11 Jun. 2003.
[19] 陳振雄,“應用希爾伯特-黃轉換之訊號濾波研究”, Journal of Science and Engineering Technology, Vol. 6, No. 1, pp. 75-84 (2010)
[20] Huang, N. E., Shen, Z. and Long, S. R., “A New View of Nonlinear Water Waves : The Hilbert Spectrum,” Annual Review of Fluid Mechanics, Vol.
31, pp. 417-457, 1999.
[21] Wu, Z. and Huang, N. E., “Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method,” Centre for Ocean-Land-Atmosphere Studies, Technical Report series, Vol. 193, No. 173, 2004.
[22] Wu, Z. and Huang, N. E., “A study of the characteristics of white noise using the empirical mode decomposition method,” Proceedings of Royal Society London, A , No. 460, pp. 1597-1611, 2004.
[23] Wu, Z. and N. E. Huang ,”Ensemble empirical mode decomposition: noise assisted data analysis method”. Advance in Adaptive Data Analysis, 2009.
[24] Wu, Z. and Huang, N. E., “Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis, Vol. 1, No. 1, pp .1-41, 2008.
[25] Alan V. Oppenheim, Ronald W. Schafer, John R. Buck, “Discrete-Time Signal Processing”, 2nd Edition ,Upper Saddle River, N.J. : Prentice Hall, 1999.
[26] Gabor, D., “Theory of communication,” Proceedings of the IEEE, Vol. 93,pp. 429-457, 1946.
[27] Zhao Zhi-Dong ,Tang Xiang-Hong, “Spectrum Analysis of Heart Sound Signal Based on Hilbert-Huang Transform”, Chinese Journal Of Sensors And Actuators, Vol.18 Mar. 2005.
[28] Gonzalez, Rafael C. , “Digital Image processing using MATLAB”, US: Gatesmark Publishing, 2009.
[29] Alan V. Oppenheim, Alan S. Willsky, S. Hamid Nawab, “Signals & Systems”, 2nd ed., Taipei : Kai Fa Book, 1983.
[30] MALLAT.S. , “Theory for Multiresolution Signal Decomposition:The Wavelet Representation” , IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 2, No. 7, July 1989.
[31] 邱創乾、林育德、劉益瑞、徐良育，“生醫訊號處理”，第五章，高材、林康平、林峰輝、陳家進主編, 生物醫學工程導論 , ISBN: 978-986-6507-03-8, 臺中：滄海書局出版, 2008.
[32] Lippincott Williams & Wilkins, “Auscultation Skills: Breath & Heart Sounds ”, 2nd ed. , Pennsylvania: Springhouse, 2001.

指導教授

蔡章仁(Jang-zern Tsai)

審核日期

2012-8-30

推文