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。

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.

[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.