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研究生:馮雲翔
研究生(外文):Feng, Yunshiang
論文名稱:基於K-SVD演算法之心律不整病徵偵測
論文名稱(外文):Detection Of Arrhythmias Based On K-SVD Algorithm
指導教授:鄭伯炤陳煥陳煥引用關係
指導教授(外文):Cheng, BochaoChen, Huan
口試委員:伍紹勳簡鳳村李昌明
口試委員(外文):Wu, SauhsuanChien, FengtsunLee, Changming
口試日期:2012-07-26
學位類別:碩士
校院名稱:國立中正大學
系所名稱:通訊工程研究所
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:2012
畢業學年度:100
語文別:中文
論文頁數:62
中文關鍵詞:ECG小波轉換稀疏表示K-SVD中值濾波So-and-Chan
外文關鍵詞:ECGwaveletsparse representationK-SVDmedian filterSo-and-Chan
相關次數:
  • 被引用被引用:0
  • 點閱點閱:525
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  • 下載下載:16
  • 收藏至我的研究室書目清單書目收藏:0
心電圖作為非侵入的心臟電信號量測方法,目前已經被廣泛地應用在心臟疾病的診斷上。在這篇論文中將基於心電圖和K-SVD演算法,提出一種心臟疾病的診斷方式,並採用MIT-BIH心律不整資料庫中的記錄來驗證所提出的方法,方法主要包含兩個部分,其中一個是使用足夠地病徵(正常)信號樣本進行特徵擷取的工作,另一部分是將偵測信號與每個病徵特徵做比較,並以最接近的特徵做為病徵偵測結果輸出。無論在特徵擷取亦或病徵偵測部分,輸入信號皆需經過去除基線飄移和消除高頻雜訊的前置信號處理,而且兩部分所採用的方式必須一致,本論文使用中值濾波器和小波轉換達成。特徵擷取部分使用K-SVD演算法訓練稀疏表示中的辭典,病徵偵測部分使用OMP方法得到之係數達成,藉由稀疏表示的概念,比較原信號與先前得到的辭典乘以係數間的平均誤差,並選擇誤差最小的辭典所代表的病徵當成輸出,最後藉由比較判斷結果與MIT-BIH心律不整資料庫所提供的病徵註解,得到病徵診斷的正確數目與誤判數目。我們所提出的方法與傳統基於時域分析與頻域分析的方法相比較,可以有效降低演算法的複雜度,且可適用於多數病徵,在複合性病徵的處理上,亦不需要額外的運算資源。
The electrocardiogram (ECG) is a non-invasive method of measuring the electrical properties of the heart, and it is widely used for diagnosis of heart disease in practice. In this study we present a method for heart disease detection based on ECG using the K-mean with Singular Value Decomposition (K-SVD) algorithm. The method we present includes two parts: feature extraction and detection for various types of heart diseases. Conventional wavelet on signal processing is used to combine with the K-SVD algorithm to perform the feature extraction and detection. In our study, the MIT-BIH arrhythmia database as a benchmark to test and verify our proposed method. In this study, we also performed pre-processing on ECG signals to achieve high accuracy on heart disease pattern detection. The pre-processing performed on ECG signals include baseline drift removal, 60-Hz interference de-nosing, median filter and wavelet transform. The K-SVD algorithm is used to construct an over-complete dictionaries and the OMP method is used to compute the coefficients with the dictionaries we derived. The disease pattern can be tested by comparing the difference among the normal signals and those heart disease patterns. Finally, we evaluated the performance in terms of the false positive and false alarm on patterns in MIT-BIH heart disease patterns. We believed that once the feature dictionary is constructed, the K-SVD algorithm is a high potential algorithm which can be implemented in portable devices to achieve reasonable processing speed as well as high accuracy on heart disease detection. Compared to those conventional frequency-domain and time-domain based analysis methods, the proposed scheme can greatly reduce the complexity of online detection by training heart disease patterns offline. As such, the proposed scheme is very suitable to be used in practice. In addition, the proposed scheme can deal with composite diseases without additional computing resource.
誌謝辭 I
摘要 III
Abstract IV
目錄 VI
圖目錄 VIII
表目錄 X
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 論文架構 2
第二章 理論背景 4
2.1 心電圖介紹 4
2.1.1 心臟傳導系統 4
2.1.2 心電圖原理與量測 5
2.1.3 ECG波形和間期 6
2.2 MIT-BIH心律不整資料庫 9
2.3 中值濾波器 12
2.4 小波轉換理論 13
2.4.1 離散小波轉換 14
2.4.2 多重解析度分解 14
2.5 So-and-Chan 15
2.6 K-SVD演算法 17
2.6.1 稀疏表示 17
2.6.2 信號的稀疏表示 18
2.6.3 正交匹配追蹤 19
2.6.4 辭典提取 21
第三章 系統架構與研究方法 25
3.1 系統架構概述 25
3.2 去除基線飄移 26
3.3 消除高頻雜訊 28
3.4 偵測R波 30
3.5 特徵擷取與病徵分析 34
第四章 模擬結果 36
4.1 迭代次數與辭典平均表示誤差 36
4.2 樣本數與辭典平均偵測錯誤 37
4.3 病徵偵測結果 39
第五章 結論與未來展望 41
參考文獻 42
附錄一 44
附錄二 46

[1]行政院衛生署:衛生統計系列(一)死因統計,http://www.doh.gov.tw/CHT2006/DM/DM2_2.aspx?now_fod_list_no=12336&class_no=440&level_no=4.
[2]Lippincott, ECG Interpretation: An Incredibly Easy! Pocket Guide, Second. Lippincott Williams & Wilkins, 2009.
[3]PhysioNet the research resource for complex physiologic signals: PhysioBank: PhysioBank ATM, http://www.physionet.org/cgi-bin/atm/ATM.
[4]R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed. Prentice Hall, 2002.
[5]J. Pan and W. J. Tompkins, “A Real-Time QRS Detection Algorithm,” Biomedical Engineering, IEEE Transactions on, vol. BME-32, no. 3, pp. 230 –236, Mar. 1985.
[6]H. H. So and K. L. Chan, “Development of QRS detection method for real-time ambulatory cardiac monitor,” in Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, 1997, vol. 1, pp. 289 –292.
[7]K. F. Tan, K. L. Chan, and K. Choi, “Detection of the QRS complex, P wave and T wave in electrocardiogram,” in Advances in Medical Signal and Information Processing, 2000. First International Conference on (IEE Conf. Publ. No. 476), 2000, pp. 41 –47.
[8]M. Elad and M. Aharon, “Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries,” Image Processing, IEEE Transactions on, vol. 15, no. 12, pp. 3736 –3745, Dec. 2006.
[9]O. Bryt and M. Elad, “Improving the k-svd facial image compression using a linear deblocking method,” in Electrical and Electronics Engineers in Israel, 2008. IEEEI 2008. IEEE 25th Convention of, 2008, pp. 533 –537.
[10]Q. Zhang and B. Li, “Discriminative K-SVD for dictionary learning in face recognition,” in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, 2010, pp. 2691 –2698.
[11]M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, 1st ed. Springer, 2010.
[12]M. Aharon, M. Elad, and A. Bruckstein, “K -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation,” Signal Processing, IEEE Transactions on, vol. 54, no. 11, pp. 4311 –4322, Nov. 2006.
[13]P. de Chazal, C. Heneghan, E. Sheridan, R. Reilly, P. Nolan, and M. O’Malley, “Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea,” Biomedical Engineering, IEEE Transactions on, vol. 50, no. 6, pp. 686 –696, Jun. 2003.
[14]MathWorks: Functions: Wavelet Families,http://www.mathworks.com/help/toolbox/wavelet/ref/f13-735230.html#f13-604597.
[15]The MathWorks: Wavelet Toolbox: Wavelet Families,http://matlab.izmiran.ru/help/toolbox/wavelet/ch06_a30.html.
[16]S. Li, Y. Ji, and G. Liu, “Optimal Wavelet Basis Selection of Wavelet Shrinkage for ECG De-Noising,” in Management and Service Science, 2009. MASS ’09. International Conference on, 2009, pp. 1 –4.

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