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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 賴飛羆(Fei-Pei Lai) | |
dc.contributor.author | Ching-Miao Lin | en |
dc.contributor.author | 林慶苗 | zh_TW |
dc.date.accessioned | 2021-06-15T11:50:29Z | - |
dc.date.available | 2016-09-13 | |
dc.date.copyright | 2016-09-13 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-08-12 | |
dc.identifier.citation | [1] Mendis, S., Puska, P., & Norrving, B. (2011). Global atlas on cardiovascular disease prevention and control. World Health Organization.
[2] Kelly, B. B., & Fuster, V. (Eds.). (2010). Promoting Cardiovascular Health in the Developing World:: A Critical Challenge to Achieve Global Health. National Academies Press. [3] Ho, T. W., Lai, H. Y., Wang, Y. J., Chen, W. H., Lai, F., Ho, Y. L., & Hung, C. S. (2014, October). A clinical decision and support system with automatically ECG classification in telehealthcare. In e-Health Networking, Applications and Services (Healthcom), 2014 IEEE 16th International Conference on (pp. 293-297). IEEE. [4] M.B. Velasco, B. Weng, K.E. Barner, A new ECG enhancement algorithm for stress ECG tests, Computers in Cardiology (2006) 917–920. [5] H. Yan, Y. Li, Electrocardiogram analysis based on the Karhunen–Loeve transform, in: Proceedings of the 3rd International Conference on Biomedical Engineering and Informatics (BMEI), vol. 2, 2010, pp. 887–890. [6] M. Popescu, P. Cristea, A. Bezerianos, High resolution ECG filtering using adaptive bayesian wavelet shrinkage, in: Computers in Cardiology, Cleveland, OH, USA, vol. 2, 1998, pp. 401–404. [7] V. Almenar, A. Albiol, A new adaptive scheme for ECG enhancement, Signal Processing 75 (1999) 253–263. [8] G. Tang, A. Qin, ECG de-noising based on empirical mode decomposition, in: 9th International Conference for Young Computer Scientists, 2008, pp. 903–906. [9] M. Alfaouri, K. Daqrouq, ECG signal denoising by wavelet transform thresholding, American Journal of Applied Sciences 5 (3) (2008) 276–281. [10] O. Sayadi, M. Shamsollahi, ECG denoising with adaptive bionic wavelet transform, in: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS ’06, 2006, pp. 6597–6600. [11] Y. Kopsinis, S. McLaughlin, Development of EMD-based denoising methods inspired by wavelet thresholding, IEEE Transactions on Signal Processing 57 (2009) 1351–1362. [12] Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm.Biomedical Engineering, IEEE Transactions on, (3), 230-236. [13] AlMahamdy, M., & Riley, H. B. (2014). Performance Study of Different Denoising Methods for ECG Signals. Procedia Computer Science, 37, 325-332. [14] Mujagic, M. Characterization of ECG Noise Sources. [15] Chen, Y. J., Ding, J. J., Huang, C. W., Ho, Y. L., & Hung, C. S. (2013, October). ECG baseline extraction by gradient varying weighting functions. In Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific (pp. 1-4). IEEE. [16] J. F. Kaiser, “Nonrecursive digital filter design using I_0-sinh window function,” in Proc. IEEE International Symposium on Circuits and Systems, San Francisco, CA, USA, Apr. 1974, pp. 20–23. [17] Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST),2(3), 27. [18] Ding, J. J., Huang, C. W., Ho, Y. L., Lin, Y. H., & Chen, Y. J., | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49819 | - |
dc.description.abstract | 心血管疾病目前是世界上最常見的死因之一。心電圖訊號是判斷心臟疾病的重要依據,並即時為病人提供醫療資源。但是這些心電圖資訊很可能因為器材操作上的錯誤、電極的感應不良、測量環境的不適當、甚至是病人的呼吸而產生雜訊。這些雜訊可能令醫生產生錯誤的診斷,對許多心電圖自動判讀系統而言,這些雜訊會讓系統擷取出錯誤的心電圖資訊,也會影響判斷疾病的正確率。所以偵測與去除心電圖中的雜訊變成一項重要的題目。在本篇研究中,我們提出了六種常見於心電圖中的雜訊型態,分別是平坦、陡峭、飽和、低震幅、相似正弦波、基線飄移等六種,每一種都分別經由不同的訊號前處理來偵測,並且針對無法復原或無法得到心電圖資訊的雜訊片段進行消除與重新組合,讓有雜訊的資料的易讀性更高。 | zh_TW |
dc.description.abstract | The Cardiovascular disease (CVDs) is one of the most common cause of death in the world. The analysis of Electrocardiograms (ECGs) is a important tools in early diagnosis arrhythmias. However sometime these measurement data would be corrupted by noises which may cause by the wrong equipment operation, poor contact of the electrode, or even the breath of the patients. These noises would make cardiologists or automatic CVDs detection system hard to make a correct diagnosis. Therefor, the noise detection and elimination from ECG data become an important project on Health Information System (HIS). In this study, we propose six most common types of noise. For each noise type detection, we apply difference signal preprocessing. If there exist some noise segments that have no information and can not be repaired, we will eliminate them and combine the remain usable segments into a complete signal. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T11:50:29Z (GMT). No. of bitstreams: 1 ntu-105-R03922105-1.pdf: 2422389 bytes, checksum: 8e9239a4ffd661fe061597c6391b2a12 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES x Chapter 1 Introduction 1 Chapter 2 Background 4 2.1 Biosignal 4 2.1.1 Electrocardiography 4 2.1.2 Electrocardiogram Grid 5 2.1.3 Features in ECG 6 2.2 Noise sources of ECG 8 Chapter 3 Method and Material 10 3.1 ECG Database Acquisition 10 3.2 Schematic Overview 12 3.3 Threshold-Based Noise Detection 13 3.3.1 System Architecture 13 3.3.2 Detection of Common Noise Types 18 3.3.3 Noise Judgment and Usable Segments 24 3.4 Post-Processing after Noise Detection 26 3.4.1 ECG R-wave peak detection 27 3.4.2 Usable Segments Combination 31 3.5 Systems for Experimental Verification 33 3.5.1 Feature Extraction 34 3.5.2 SVM Classification 36 Chapter 4 Experimental Results 40 4.1 Individual Performance in Noise Detection 40 4.1.1 FLAT and STEEP detector 40 4.1.2 SA detector 42 4.1.3 LA detector 43 4.1.4 BLD detector 44 4.1.5 Sine wave-like detector 46 4.1.6 Summary of Performance 47 4.2 Improvement of Disease Detection System 48 Chapter 5 Conclusion and Future Work 50 5.1 Conclusion 50 5.2 Future work 51 REFERENCE 52 | |
dc.language.iso | en | |
dc.title | 基於閘值偵測與排除心電圖雜訊系統 | zh_TW |
dc.title | Threshold based system of noise detection and elimination for ECG signal | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 汪大暉,蔡坤霖,陳啟煌,周迺寬 | |
dc.subject.keyword | 訊號處理,心電圖,心電圖雜訊,閘值,雜訊偵測,雜訊排除, | zh_TW |
dc.subject.keyword | Signal processing,ECG,Electrocardiograms,ECG noise,noise detection,noise elimination, | en |
dc.relation.page | 54 | |
dc.identifier.doi | 10.6342/NTU201602311 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2016-08-12 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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