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dc.contributor.authorChen, Shiue Ruen_US
dc.identifier.citation[1] Barbara Aehlertm 原著/王怡心醫師編譯, 輕鬆掌握心電圖, 合計圖書出版, 台北, 2003. [2] E. Braunwald, 台北榮民總醫院內科部醫師李哲全編譯, 圖解心臟疾病學精要, 合記圖書出版社, 2002. [3] 高謙次, 杜賓, 心電圖速成(簡易判別圖法), 南山堂出版, 鴻文堂總經銷, 臺北市, 1991. [4] 楊正榮, A Method of QRS Detection Based on Wavelet Transforms, 國立中山大學碩士論文, 2004. [5] 王元宏, Electrocardiogram Signal for the Detection of Obstructive Sleep Apnoea Via Artificial Neural Networks, 國立中山大學碩士論文, 2004. [6] H.-B. Li, K.-I. Takizawa, B. Zheri, and R. Kohno, “Body area network and its standardization at IEEE 802.15.MBAN,” in Proc. Mobile and Wireless Communications Summit, Jul. 2007, pp. 1-5. [7] J. Penders, B. Gyselinckx, R. Vullers, M. De Nil, V. Nimmala, J. van de molengraft, F. Yazicioglu, T. Torfs, V. Leonov, P. Merken, and C. Van Hoof , “Human++:from technology to emerging health monitoring,” in Proc. International Symposium on Summer School Medical Devices and Biosensors, June 2008, pp.94-98. [8] B. Gyselinckx, C. Van Hoof, J. Ryckaert, R.F. Yazicioglu, P. Fiorini, and V. Leonov, “Huamn++:autonomous wireless sensors for body area networks,” in Proc. Custom Integrated Circuits Conference, Sept. 2005, pp. 13–19. [9] B. Gyselinckx, R.Vullers, C.V. Hoof, J. Ryckaert, R.F. Yazicioglu, P. Fiorini, and V. Leonov, “Humm++:Emerging Technology for Body Area Networks,” in Proc. IFIP International Coference on Very Large Scale Integration, Oct. 2006, pp. 175-180. [10] J. Yuan, K.K. Tan, and T.H. Lee, “Development of an e-Guardian for the Single Elderly or Chronically-Ill Patientss,” in Proc. Iternational Conference on Communications and Mobile Computing, Apr. 2010, pp. 378–382. [11] M. Marzencki, B. Hung, P. Lin, Y. Huang, T. Cho, Y. Chuo, and B. Kaminska,“ Context-aware physiological data acquisition and processing with wireless sensor networks,” in Proc. IEEE International Workshop on Medical Measurements and Applications, May 2010, pp.53-56. [12] Z. Shen, C. Hu, J. Liao, and H. Meng, “An algorithm of premature contraction detection based on wavelet method,” in Proc. IEEE International Conference on Information and Automation, June 2010, pp. 1053–1058. [13] S. Nahar and M. ShahNoor bin Munir, “Automatic detecion of premature ventricular contraction beat using Morphological Transformation and cross-correlation,” in Proc. Iternational Conference on Signal Precessing and Communication Syatems, Sept. 2009, pp. 1-4. [14] P.R. Gomes, F.O. Soares, J.H. Correia, and C.S. Lima, “Cardiac arrhythmia classification using Wavelets and Hidden Markov Models - a comparative approach,” in Proc. Annual International Conference of IEEE Enginering in Medicine and Biology Society, Sept. 2009, pp. 4727-4730. [15] O. Alptekin and A. Akan, “Detection of some heart diseases by the analysis of ECG signals,” in Proc. Signal Precessing and Communications Applications Conference, April. 2010, pp. 716-719. [16] A. Pachauri and M. Bhuyan, “Wavelet and energy based approach for PVC detection,” in Proc. Iternational Conference on Emergin Trends in Electronic and Photonic Devices & Systems, Dec. 2009, pp. 258-261. [17] X. Zheng, Z. Li, L. Shen, and Z. Ji, “Detection of QRS complexes based on Biorthogonal Spline Wavelet, ” in Proc. International Symposium on Information Science and Engineering, Dec. 2008, pp. 502-506. [18] Y. Yang, X. Huang, and X. Yu, “Real-time ECG monitoring system based on FPGA,” in Proc. Annual Conference of the IEEE Industrial Electronics Society, Nov. 2007, pp. 2136-2140. [19] R. Trobec, M. Depolli, and V. Avbelj, “Wireless Network of Bipolar Body Electrodes,” in Proc. Inernational Conference on wireless On-demand Network Systems and Services, Feb. 2010, pp. 145-150. [20] [21] [22] [23] 林羣晨,A cardiac health expert system based on electrocardiogram, 慈濟大學碩士論文,2007。zh_TW
dc.description.abstract在台灣,心臟疾病長期以來都位於十大死因排行中,近幾年更高居第二,因此診斷心臟疾病及如何預防就顯得格外重要。心電圖(ECG)是目前判斷心臟活動最可靠的方法,藉由紀錄心臟活動的相關電氣訊號,可在心電圖紙上畫出心電圖,再由醫師判斷心臟是否有異常,進而評估並加以治療。心臟疾病很多是瞬間的或發生時間極短的,這會造成當感到心臟不舒服時,馬上趕到醫院就醫,做了心電圖檢查,卻檢查不出是什麼原因,而讓醫生無法評估與治療,所以一種高精確度即時偵測系統是迫切需要的,以避免上述情形。本論文的重點是提出一個高精確度即時心室早期收縮(Premature Ventricular Contraction ,PVC)偵測系統。採用小波轉換偵測R波波峰,並提出一個全新的結合了兩種方法之PVC偵測演算法來加以偵測、判斷有無PVC的發生,第一種為波谷之和法,第二種為R_peak與最小值之和法,若發生病態則發出警告訊息給使用者。模擬與驗證採用MIT-BIH Arrhythmia Database (mitdb),最後使用FPGA實現我們的系統。zh_TW
dc.description.abstractIn Taiwan, heart disease has been in the top ten causes of death for a long time, and even at the second place in recent years, Thus the diagnosis of heart disease and how to prevent it is particularly important.Currently, Electrocardiogram (ECG) is the most reliable way to determine heart activity by record relevant electrical signal, which can be drawn on electrocardiogram paper to produce ECG. Doctor can diagnose whether there is abnormal, and further assess or treat.A lot of heart diseases occur in a moment or a very short time, and it will cause the patients to feel uncomfortable and then to go to the hospital to do ECG examination, but can not check out the reason so that the doctors can not assess and treat. Therefore, a high-precision real-time detection system is urgently needed to prevent the above situation.The focus of this thesis is to propose a high-precision real-time Premature Ventricular Contraction (PVC) detection system. We will use wavelet transform to detect R wave peaks and propose a new PVC detection algorithms that combines two methods to detect and determine whether the occurrence of PVC. The first method is the sum of trough and the second one is the sum of R_peak and minimum. If the morbid state happens, a warning message will be sent to the user.We simulate and verify the proposed system by using MIT-BIH Arrhythmia Database (mitdb). Finally, our system is implemented by FPGA.en_US
dc.description.tableofcontents致謝 I 摘要 II ABSTRACT III 目錄 IV 圖目錄 VI 表目錄 IX 第一章 緒論 1 1-1 簡介 1 1-2 研究動機與方法 1 1-3 內容大綱 2 第二章 ECG與MIT-BIH DATABASE簡介 3 2-1 心電圖 3 2-1-1 心臟結構與心電信號的傳導 4 2-1-2 十二導聯心電圖 7 2-2 MIT-BIH DATABASE 8 2-2-1 MIT-BIH Arrhythmia Database 讀取 9 2-2-2 MIT-BIH Arrhythmia Database 病症判讀 11 2-2-3 心室早期收縮簡介 13 第三章 小波轉換 14 3-1 小波轉換 14 3-1-1 小波轉換基本理論 16 3-1-2 離散小波轉換 18 3-2 MALLAT快速演算法與LIPSCHITZ指數 20 3-2-1 Mallat快速演算法與小波濾波器係數 20 3-2-2 Lipschitz指數α 23 第四章 PVC偵測系統 25 4-1 PVC偵測系統架構 25 4-2 R波波峰偵測 25 4-3 PVC偵測 31 4-3-1 PVC偵測演算法之Method_1 :波谷之和 31 4-3-2 PVC偵測演算法之Method_2 :R_peak與最小值之和 33 4-3-4 PVC偵測演算法軟體模擬結果 34 4-3-5 Sending warning 機制模擬結果 38 4-4 PVC偵測演算法比較 39 4-4-1 我們的系統(proposed sysytem) 39 4-4-2 “Wavelet and Energy Based Approach for PVC Detection”系統 41 4-5 兩系統比較 44 第五章 硬體設計與架構 47 5-1系統設計與硬體架構 47 5-2 FPGA驗證 51 第六章 結論 63 參考文獻 64zh_TW
dc.subjectwavelet transformen_US
dc.titleA High-Precision Real-Time Premature Ventricular Contraction (PVC) Detection System Based on Wavelet Transformen_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
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