臺灣博碩士論文加值系統

心電圖在測量的過程中經常容易被許多的雜訊干擾，而這些雜訊往往會影響心電圖的判斷或造成特徵萃取的困難。因此我們在研究中以希爾伯特-黃轉換(HHT)的第一部份，也就是經驗模組分解(EMD)來進行濾波處理。在濾波的過程中，發現EMD的濾波方法會犧牲掉心電圖一部份的低頻成份。因此，我們試著使用總體經驗模組分解(EEMD)的方法來做心電圖的濾波。並以Signal-to-Error Ratio (SER)來做誤差估測，結果EEMD方法的效果的確較優於EMD方法的濾波器也較傳統Butterworth濾波器要好。在完成心電圖的濾波處理後我們將脈搏傳輸時間(PTT)計算出來。從PTT再發展出一些新的PTT衍生參數，將參數應用在盤腿實驗當中。比較心率變異(HRV)參數與PTT衍生參數，發現PTT衍生參數比起HRV參數在盤腿坐姿的改變上有更多的差異性。當坐姿改變時，HRV參數只有RRI有差異(P<0.05)，而PTT衍生參數中PTT、PTT_SDNN、PTT_SD2、PTT_HF…等參數皆有明顯的差異(P<0.05)。這個結果顯示PTT比HRV更適合於姿勢變化的研究。在日後的研究中可以將PTT衍生參數應用到更廣的研究範圍，相信定能獲得令人新奇的發現。

Electrocardiogram in survey process frequently easily by many noise disturbances, and these noise will often affect the electrocardiogram the diagnosis or let the feature extract have a difficulty. Therefore we using the first part of Hilbert - Huang transform (HHT), the empirical mode decomposition (EMD) to filter the noise. In the process, we discovered that the filter of EMD method will sacrifice a part of the low frequency ingredient. So, we try to use the ensemble empirical mode decomposition (EEMD) to as the filter. And makes the error by Signal-to-Error Ratio (SER) to estimate, finally the effect of EEMD method indeed surpasses the EMD method and also better than traditional Butterworth filter. After completing the electrocardiogram filter processing we calculate pulse transit time (PTT). We try to develop some new PTT derivation parameter from PTT, applying the parameter in the experiment of Leg-Crossing. Compared with the HRV parameter and the PTT derivation parameter, discovered that the PTT derivation parameter to have more differences than the HRV parameter in posture change. When posture change, the HRV parameter only the RRI has difference (P<0.05), But in PTT derivation parameter PTT, PTT_SDNN, PTT_SD2, PTT_HF all have the obvious difference (P<0.05). This result showed that PTT more suitable to HRV in the posture change research. In future may apply the PTT derivation parameter to the broader range of study, we believed that can obtain more interesting discoveries surely.

中文摘要 i
英文摘要 ii
目錄 iii
圖目錄 v
表目錄 vii
第一章 緒論 1
1.1 前言 1
1.2 心電圖介紹 3
1.2.1 心臟之生理構造 3
1.2.2 心電圖之量測 5
1.2.3 心電圖之雜訊類型與濾波 9
1.3 脈搏波介紹 10
1.3.1 脈搏波之生理特性 10
1.3.2 脈搏傳輸時間介紹 11
1.3.3 脈搏傳輸時間應用 12
第二章 希爾伯特-黃轉換及濾波處理 13
2.1 希爾伯特-黃轉換理論簡介 13
2.2 經驗模組分解原理 14
2.3 總體經驗模組分解原理 18
2.4 應用訊號重建於濾波 19
2.4.1 應用EMD方法於ECG濾波 20
2.4.2 應用EEMD方法於ECG濾波 23
2.5 濾波效果 25
第三章 脈搏傳輸時間與盤腿關係之實驗設計 29
3.1 盤腿實驗設計 29
3.1.1 硬體架構 29
3.1.2 實驗流程 32
3.2 特徵參數介紹 34
3.2.1 心電圖QRS複合波之波峰偵測 34
3.2.2 脈搏波之波峰偵測 36
3.2.3 心率變異參數介紹 36
3.2.4 脈搏傳輸時間衍生參數介紹 38
3.3 統計分析：t-test 40
第四章 結果與討論 41
4.1 心律變異之參數結果 41
4.2 脈搏傳輸時間及其衍生參數之結果 43
4.2.1 四肢之間的差異 45
4.2.2 姿勢之間的差異 47
4.3 討論 50
第五章 結論與未來展望 54
第六章 參考文獻 56
致謝 60

[1] S. Akselrod, D. Gordon, F. A. Ubel, D. C. Shannon, A. C. Barger and R. J. Cohen, “Power spectrum analysis of heart rate fluctuation : a quantitative probe of beat-to-beat cardiovascular control,” Science, Vol. 213, No. 10, 1981, pp. 220-222.
[2] Z. S. N. E. Huang, S. R. Long, M. C. Wu, E. H. Shih, Q. Zheng, C. C. Tung and H. H. Liu, “The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis,” Proc. Roy. Soc. London, Vol. 454, 1998, pp. 903-995.
[3] J. Y. Foo, S. J. Wilson, G. R. Williams, M. A. Harris, D. M. Cooper, “Pulse transit time changes observed with different limb positions,” Physiol. Meas., Vol. 26, No. 6, Dec 2005, pp. 1093–1102.
[4] J. Y. Foo, ”Normality of upper and lower peripheral pulse transit time of normotensive and hypertensive children,” J Clin Monit Comput., vol. 21, No. 4, Aug 2007, pp. 243-248.
[5] J. Y. Foo, C.S. Lim, “Changes induced in the lower- and upper-limb pulse transit-time ratio during inspiratory resistive breathing,” Biomed Tech (Berl)., Vol. 52, No. 3, 2007, pp. 248-54.
[6]Stuart Ira Fox，王錫崗等譯，人體生理學HUMAN PHYSIOLOGY(第九版)。台灣：美商麥格羅．希爾國際出版公司，2006年5月。
[7]大英簡明百科，心臟，http://sc.hrd.gov.tw/ebintra/Content.asp?Query=2&ContentID=10729，2002
[8]武漢體育學院，心臟的機能，http://lib.wipe.edu.cn/shengli/wlkc/zj3/zj3-1.htm，2002
[9]加拿大紐芬嵐健康食品有限公司中文海豹油網 (DPA,DHA,EPA)，血管狀態，http://www.omega3plusdpa.com/Heart_info/BloodVessel.htm
[10]Plattsburgh State University, ECG leads – diagram, http://faculty.plattsburgh.edu/david.curry/NUR464/Links_Fall_2008.htm, 2005
[11]M. N. Milanesi M, Vanello N, Positano V, Santarelli MF, Landini L, “Independent component analysis applied to the removal of motion artifacts from electrocardiographic signals,” Med Biol Eng Comput, Vol. 46, Vo. 3, Mar 2008, pp. 251-261.
[12]M. P. S. Chawla, Verma, H K, Kumar, Vinod, “Artifacts and noise removal in electrocardiograms using independent component analysis,” Int J Cardiol, Vol. 129, No. 2, Sep 2008, pp. 278-281.
[13]R. R. Y. Wu , Y. Zhou , S. Ng, “Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system,” Medical Engineering & Physics, Vol. 31, No. 1, May 2008, pp. 17-26.
[14]K. N. Poornachandra S, “A wavelet coefficient smoothened RLS adaptive denoising model for ECG,” Biomed Sci Instrum, Vol. 39, 2003, pp. 154-157.
[15]E. E, “Electrocardiogram signals de-noising using lifting-based discrete wavelet transform,” Comput Biol Med, Vol. 34, No. 6, Sep 2004, pp. 479-493.
[16]H. T. Ma, Y. T. Zhang, “Spectral analysis of pulse transit time variability and its coherence with other cardiovascular variabilities,” Conf. Proc. IEEE Eng. Med. Biol. Soc. , Vol. 1, 2006 pp. 6442-6445.
[17]S. E. Brietzke, E. S. Katz, D. W. Roberson, “Pulse transit time as a screening test for pediatric sleep-related breathing disorders,” Arch. Otolaryngol. Head Neck Surg., Vol. 133, No. 10 , 2007, pp. 980-984.
[18]Y. Xiao, X. Zhong, R. Huang, “Use of pulse transit time as a measure of autonomic arousals in patients with obstructive sleep apnea,” Chin. Med. Sci. J., Vol. 22, No. 2, 2007, pp. 89-92.
[19]B. C. Galland, E. Tan, B. J. Taylor, “Pulse transit time and blood pressure changes following auditory-evoked subcortical arousal and waking of infants,” Sleep, Vol. 30, No.7 , 2007, pp. 891-897.
[20]J. L. Pépin, N. Delavie, I. Pin, C. Deschaux, J. Argod, M. Bost, P. Levy, “Pulse transit time improves detection of sleep respiratory events and microarousals in children,” Chest, Vol. 127, No. 3, 2005, pp. 722-730.
[21]J. Y. Foo, S. J. Wilson, A. P. Bradley, G. R. Williams, M. A. Harris, D. M. Cooper, “Use of pulse transit time to distinguish respiratory events from tidal breathing in sleeping children,” Chest, Vol. 128, No. 4, 2005, pp. 3013-3019.
[22]X. Y. Zhang, Y. T. Zhang, “The effect of local mild cold exposure on pulse transit time,” Physiol. Meas. , Vol. 27, No. 7, 2006, pp. 649-660.
[23]G. S. Chan, P. M. Middleton, B. G. Celler, L. Wang, N. H. Lovell, “Change in pulse transit time and pre-ejection period during head-up tilt-induced progressive central hypovolaemia,” J Clin. Monit. Comput. , Vol. 21, No. 5, 2007, pp. 283-293.
[24]P. R. Flandrin, G. Goncalves, P, “Empirical mode decomposition as a filter bank,” IEEE Signal Process Lett, Vol. 11, No. 2, Feb 2004, pp. 112- 114.
[25]H. N. E. Wu Z, “Ensemble empirical mode decomposition:a noise-assisted data analysis method,” Center for Ocean-land-Atmosphere Studies, Vol. 1, No. 1, 2009, pp. 1-41.
[26]P. S. Hamilton and W. J. Tompkins, “Quantitaive investigation of QRS detectionrules using the MIT/BIH arrhythima database,” IEEE Transaction on Biomedical Engineering, Vol. 33, No. 12, 1986, pp. 1157-1165.
[27]Sheldon P. Gordon，Florence S. Gordon，李弘斌等譯，統計學(初版)。台灣：美商麥格羅．希爾國際出版公司，1995年6月。