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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 郭德盛(Te-Son Kuo) | |
dc.contributor.author | Hsiao-Hsuan Chou | en |
dc.contributor.author | 周曉璇 | zh_TW |
dc.date.accessioned | 2021-06-13T06:04:19Z | - |
dc.date.available | 2008-06-28 | |
dc.date.copyright | 2006-06-28 | |
dc.date.issued | 2006 | |
dc.date.submitted | 2006-06-16 | |
dc.identifier.citation | [1] H. Lee and K. M. Buckley, “ECG data compression using cut and align beats approach and 2-D transforms,” IEEE Trans. Biomed. Eng., 46, 5, pp. 556-565, May, 1999.
[2] G. Nave and A. Cohen, “ECG compression using long-term prediction,” IEEE Trans. Biomed. Eng., 40, 9, pp. 877-885, Sep. 1993. [3] B. Wang and G. Yuan, ”Compression of ECG data by vector quantization,” IEEE Trans. Biomed. Eng., 40, pp. 23-26, Jul. 1997. [4] J. R. Cox, F. M. Noile, H. A. Fozzard, and G. C. Olover, “AZTEC: 1-D to 2-D processing program for real-time ECG rhythm analysis,” IEEE Trans. Biomed. Eng., 15, pp. 128-129, Apr. 1968. [5] M. Ishijima, S. B. Shin, G. H. Hostetter, and J. Sklansky, “Scan-along polygonal approximation for data compression of electrocardiograms,” IEEE Trans. Biomed. Eng., 30, 11, pp. 723-729, Nov. 1983. [6] L. N. Bohs and R. C. Barr, “Prototype for real-time adaptive sampling using the fan algorithm,” IEEE Trans. Biomed. Eng., 26, pp. 574-583, Nov. 1988 [7] R. C. Barr, “Adaptive sampling of cardiac waveforms,” J. Electrocard., 21, pp. S57-S60, 1988. [8] A.C. D’ambrosio, A. Ortiz-conde, and F. J. Sanchez, “Percentage area difference (PAD) as a measure of distortion and its use in Maximum Enclosed Area (MEA), a new ECG signal compression algorithm,” 4’th IEEE Int. Caracas Conf. on Devices, Circuits Syst., pp. 1035-1-1035-5, 2002. [9] D. Haugland, J. G. Herber, and J. H. Husoy, “Optimization algorithm for ECG data compression,” Med. Biol. Eng. Comput., 35, pp. 420-424, 1997. [10] J. G. Herber, D. Haugland, and J. H. Husoy, “An efficient implementation of an optimal time domain ECG coder,” 18’th IEEE Engineering in Medicine and Biology Society, Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference, 4, pp 1384-1385, Nov. 1996 [11] J. J. Wei, C. J. Chang, N. K. Chou, and G. J. Jan, “ECG data compression using truncated singular value decomposition,” IEEE Trans. Biomed. Eng., 5, pp. 290-299, Dec. 2001. [12] M. L. Hilton, “Wavelet and wavelet packet compression of electrocardiograms,” IEEE Trans. Biomed. Eng., 44, pp. 394-402, May 1997. [13] A. R. A. Moghaddam and K. Nayebi, “A two dimensional wavelet packet approach for ECG compression,” Signal Processing and its Applications, the 6th International, Symposium, pp. 226-229, Aug. 2001. [14] S. C. Tai, C. C. Sun, and W. C. Tan, ” 2-D ECG compression method based on wavelet transform and modified SPIHT,” IEEE Trans. Biomed. Eng., 52, 6, pp. 999-1008, Jun. 2005. [15] R. Benzid, F. Marir, A. Boussaad, M. Benyoucef, and D. Arar, “Fixed percentage of wavelet coefficients to be zeroed for ECG compression,” Electronics Letters, 39, 11, pp. 830-831, 2003. [16] M. Blanco-Velasco et. al, “A low computational complexity algorithm for ECG signal compression,” Medical Engineering and Physics, 26, 7, pp. 553-568, Sep. 2004. [17] J. Vander, J. H. Sherman, and D. S. Luciano, Human Physiology, McGraw-Holl, 6th ed, chap 14, pp 393-472, 1994. [18] R. Granit, Bioelectromagnetism Bio-electromagnetism Principles and Applications of Bioelectric and Biomagnetic Fields, chap 9, 15, New York Oxford University Press, 1995. [19] J. Lee, ECG Monitoring in Theatre, Royal Devon and Exeter Hospital, Exeter, UK - Previously: Ngwelezana Hospital, Empangeni, Kwa-zulu Natal. [Online] Available: http://www.nda.ox.ac.uk/wfsa/html/u11/u1105_01.htm#ecgm [20] H. H. Chou, Y. J. Chen, and T. S. Kuo, 'An Adaptive Real-time ECG Sampling Algorithm Using Sum-squared Difference,' The 12th International Conference on Biomedical Engineering (IEEE ICBME 2005), Suntec Singapore International Convention and Exhibition Center, SINGAPORE, Dec. 2005. [21] H. H. Chou, Y. J. Chen, Y. C. Shiau, and T. S. Kuo, 'An Effective and Efficient Compression Algorithm for ECG with Irregular Periods', IEEE Trans. Biomed. Eng., 53, 6, pp. 1198-1205, Jun. 2006 [22] D. S. Taubman and M. W. Marcellin, JPEG2000: image compression fundamentals, standards, and practice, Boston, Kluwer Academic Publishers, 2002. [23] M. D. Adams, An official reference implementation of the JPEG-2000 Part-1 codec. The Jasper Project. [Online] Available: http://www.ece.uvic.ca/~mdadams/jasper/ [24] A. Bilgin, M. W. Marcellin, and M. I. Altbach, “Compression of electrocardiogram signals using JPEG2000,” IEEE Tran. Consumer Electronics, 49, pp.833–840, Nov. 2003. [25] A. Bilgin, M. W. Marcellin, and M. I. Altbach, “Wavelet compression of ECG signals by JPEG2000,” Conf. Data Compression, DCC2004, pp. 527 – 527, Mar. 2004. [26] A. Said and W. A. Pearlman, “A new, fast, and efficient image codec based on set partitioning in hierarchical trees,” IEEE Trans. Circuits Syst. Video Technol., 6, 3, pp. 243-250, Jun. 1996. [27] “MIT-BIH Arrhythmia Database CD-ROM,” 2nd. Ed., Harvard-MIT Division of Health Sciences and Technology, Aug. 1992. [28] B. U. Kohle, C. Hennig, and R. Orglmeister, “The principles of software QRS detection,” IEEE Biomed. Eng. Mag., pp 42-57, Jan.-Feb. 2002. [29] J. Lee, K. Jeong, J. Yoon, and M. Lee, “A simple real-time QRS detection algorithm,” IEEE Proc. Biomed. Eng., 4, pp. 1396–1398, Oct. 1996. [30] Wavelets Theory, CeBIT Hannover, Germany 2006 [Online] Available: http://www.wave-report.com/tutorials/Wavelets.htm [31] M. E. Wall, A. Rechtsteiner, and L. M. Rocha. 'Singular value decomposition and principal component analysis,' 2002 [Online] Available: http://public.lanl.gov/mewall/kluwer2002.html [32] D. P. Berrar, W. Dubitzky, M. Granzow, A Practical Approach to Microarray Data Analysis, Kluwer: Norwell, MA, pp. 91-109, 2003. [33] B. A. Rajoub, “An efficient coding algorithm for the compression of ECG signals using the wavelet transform,” IEEE Trans. Biomed. Eng., 49, pp. 355-362, Apr. 2002. [34] A. Alshamali and A. S. Al-Fahoum, “Comments on An Efficient Coding Algorithm for the Compression of ECG Signals Using the Wavelet Transform,” IEEE Trans. Biomed. Eng., 50, 8, pp. 1034-1037, Aug. 2003 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/34350 | - |
dc.description.abstract | 現代的心電圖監視系統產生大量的資料,需要巨大記憶容量,為了有效率地處理、傳輸、及儲存這些資料,文獻上已有許多心電圖壓縮演算法被提出。然而大部份的演算法較適用於規律心電圖,但用在不規則週期心電圖中,壓縮率就沒有規律心電圖那麼好。本文在時域及轉換域各提出一種較佳壓縮法,可將不規則週期心電圖壓縮得比文獻上的其他同類型方法更好。
在時域壓縮方面,本文提出一種新穎且快速的非均勻取樣演算法,應用於心電圖的壓縮,比之前文獻上的其他時域壓縮演算法壓誤差更小。它計算複雜度低,可達到即時取樣的要求,而且,即使在不規則的心電圖中,仍能保持穩定的壓縮率及信號品質。本方法首度利用誤差方和 (SSD) 為測試公式,將此公式計算結果限制在某設定值以下,並應用於 MIT-BIH 心電圖資料庫,此資料庫以 11位元解析度及 360 Hz 取樣頻率來儲存心電圖資料。跟本方法一樣利用限制誤差取樣的演算法有 FAN,SAPA,MEA等。一般評估此類取樣演算法的指標有取樣壓縮率 (SCR) 及誤差方均根百分比 (PRD) ,我們的演算法比起上述方法,有更高的壓縮率,更低的誤差,且可保存更佳的心電圖臨床特性。 在轉換域壓縮方面,本文提出一完整程序一步步增加壓縮效能。首先利用心電圖中有心跳週期之內與之間的關連性,選擇一種 QRS 偵測演算法,將每一心跳QRS峰值鑑別出來,平移到平坦區域切分每個心跳。再依週期長短排序,使原雜亂無章的心跳片段變得有秩序。此週期排序步驟,是其他文獻未曾提出的創新,且對不規律心電圖壓縮,效能強大。接著用均值等化或週期等化處理成較平滑的二維矩陣。最後選用先進的靜態影像壓縮器JPEG2000來壓縮,得到良好的結果,即獲得較大壓縮率 (CR) ,較小誤差 (PRD) ,及較小的局部誤差 (MaxErr and StdErr)。此程序針對不規則週期心電圖的壓縮結果與之前文獻相比,突顯出很大的進步。與其他壓縮演算法的步驟合併使用,還能增進其在不規則週期心電圖的壓縮效能。 | zh_TW |
dc.description.abstract | Because modern Electrocardiogram (ECG) monitoring devices generate vast amounts of data and require huge storage capacity, many ECG compression methods have been proposed to process, transmit, and store the data efficiently. Most of the related papers showed fair ECG compression performances for regular ECG cases. However, their compression performance dropped in irregular ECG waveforms. In fact, the abnormal ECG signals have more clinic significance. In this dissertation, we propose improved time-domain and transform-domain compression algorithms separately for ECG signals with irregular periods.
For the time domain, a novel and rapid ECG signal compression algorithm with less error for non-uniform sampling is proposed. It meets the real-time requirements for clinical applications. Moreover, the compression performance is stable even for abnormal ECG signals. A criterion called the Sum Squared Difference (SSD) is first defined as an error test equation. The algorithm using SSD to calculate error tolerance is applied to the records in the MIT-BIH 11-bit resolution database that was based on a 360 Hz sampling rate. It belongs to the threshold-limited algorithm such as the popular Fan algorithm but outperforms the Scan-Along Polygonal Approximation (SAPA), the Fan, and the Maximum Enclosed Area (MEA) algorithms in Sample Compression Ratio (SCR) and the Percent Root mean squared Difference (PRD). In addition, it maintains more clinical features of the ECG signals. For the transform domain, this dissertation presents an effective and efficient algorithm for compressing ECG signals by exploiting their inter- and intra-beat correlations. To better reveal the correlation structure, the ECG signals are converted into a proper 2-D array. This involves a few steps including QRS detection and alignment, period sorting, and length equalization. Of all the steps, period sorting has been first proposed by us as a novel and powerful method to reduce period differences among heartbeats effectively. Then the state-of-the-art JPEG2000 is selected for its high efficiency and flexibility. In this way, the proposed algorithm is shown to outperform existing methods in the literature by simultaneously achieving high Compression Ratio (CR) and low PRD. Furthermore, because the proposed period sorting method rearranges the detected heartbeats into an orderly array that is easier to compress, this algorithm is insensitive to irregular ECG periods. This is a significant improvement over existing 2-D ECG compression methods. This algorithm can be combined with other algorithms or codecs to improve their efficiency. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T06:04:19Z (GMT). No. of bitstreams: 1 ntu-95-D86921033-1.pdf: 4182573 bytes, checksum: c613a903de2902cd939061520c0501e8 (MD5) Previous issue date: 2006 | en |
dc.description.tableofcontents | 摘要
第一章 緒論………………………………………………………………………… 1 1-1 研究動機與目的 1-2 心電圖簡介 1-2-1 心電圖 1-2-2 心電圖診斷的應用 1-3 心電圖壓縮在不規則週期心電圖的文獻探討 1-4 本文架構 第二章 原理…………………………………………………………………………4 2-1 時域壓縮演算法 2-1-1 時域壓縮演算法之介紹 2-1-2 效能量度 2-1-3 誤差方和 2-1-4 可調式閥值 2-2 轉換域壓縮演算法 2-2-1 轉換域壓縮演算法之介紹 2-2-2 效能量度 2-2-3 演算法整體介紹 2-2-4 QRS 偵測與對齊 2-2-5 週期排序 2-2-6 長度等化 2-2-7 編碼解碼器 第三章 結果…………………………………………………………………………10 3-1 時域壓縮演算法 3-2 轉換域壓縮演算法 第四章 討論…………………………………………………………………………11 4-1 時域壓縮演算法 4-2 轉換域壓縮演算法 4-2-1 PRD 計算之歸零調整 4-2-2 記錄心跳順序與長度所造成之負荷 4-2-3 對 QRS 偵測準確性之敏感度 4-2-4 無失真壓縮之可行性 4-2-5 時間延遲 4-3 時域與轉換域演算法之比較 第五章 結論與未來方向…………………………………………………………14 5-1 時域壓縮演算法 5-1-1 結論 5-1-2 本演算法之重要貢獻 5-1-3 未來方向 5-2 轉換域壓縮演算法 5-2-1 結論 5-2-2 本演算法之重要貢獻 5-2-3 未來方向 參考文獻………………………………………………………………………………79 附錄……………………………………………………………………………………83 Abstract Chapter 1 Introduction……………………………………………………………1 1-1 Motivation and Purpose……………………………………………………….1 1-2 Introduction of Electrocardiogram…………………………………………….3 1-2-1 Electrocardiogram……………………………………………………3 1-2-2 The Application Areas of ECG Diagnosis……………………….….11 1-3 Literature of Compression Algorithms in ECG with Irregular Periods…....14 1-4 Organization of the Dissertation…………………………………………….14 Chapter 2 Methods………………………………………………………………...….15 2-1 Time Domain……………………………………………………….……..…15 2-1-1 Introduction of Direct Time-Domain Techniques……………..……15 2-1-2 Performance Measures………………………………………….…..21 2-1-3 Sum Squared Difference……...………………………………...….22 2-1-4 Adaptive Threshold………………………………………………. 24 2-2 Transform Domain…………………………………………………………25 2-2-1 Introduction of Transform-Domain Techniques………………..…..25 2-2-2 Performance Measures…………………………………..………… 27 2-2-3 Overview of the Algorithm………………………………...…..……29 2-2-4 QRS Detection and Alignment………………………………….31 2-2-5 Period Sorting……………………………………………………..32 2-2-6 Length Equalization……………………………………………….33 2-2-7 Codec……………………………………………………………….40 Chapter 3 Results…………………………………………………………………….41 3-1 Time Domain………………………………………………………………41 3-2 Transform Domain…………………………………………………………48 Chapter 4 Discussions..………………………………………………………….…...60 4-1 Time Domain………………………………………………………….…....60 4-2 Transform Domain………………………………………….……….………61 4-2-1 Offset in PRD Calculation……………………………….………..61 4-2-2 Overhead of File Header and Extra Indexing Information………... 63 4-2-3 Sensitivity to Accuracy of QRS Detection……………...…………63 4-2-4 Feasibility of Lossless Compression…………………….…………64 4-2-5 Time Latency…………………………………………….……..…..70 4-3 Comparisons between Proposed Time-Domain and Transform-Domain Algorithms……………………………………………………………….....70 Chapter 5 Conclusions and Future Work……………………………………….…...72 5-1 Time Domain………………………………………………………………72 5-1-1 Conclusions………………………………………………………..72 5-1-2 Significant Algorithm Contribution………………………………..73 5-1-3 Future Work……………………………………………………….74 5-2 Transform Domain…………………………………………………………75 5-2-1 Conclusions………………………………………………..………75 5-2-2 Significant Algorithm Contribution………………………………..77 5-2-3 Future Work……………………………………………………….78 References………………………………………………………………….…………79 Appendix…………………………………………………………………….…………83 | |
dc.language.iso | en | |
dc.title | 時域及轉換域上針對不規則週期心電圖之壓縮演算法 | zh_TW |
dc.title | Time-Domain and Transform-Domain Compression Algorithms for ECG Signals with Irregular Periods | en |
dc.type | Thesis | |
dc.date.schoolyear | 94-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 貝蘇章(Soo-Chang Pei),李百祺(Pai-Chi Li),周迺寬(Nai-Kuan Chou),楊順聰(Shuen-Tsung Yang),張寶基(Pao-Chi Chang),趙福杉(Fu-Shan Jaw) | |
dc.subject.keyword | 心電圖,壓縮,不規則,週期,排序, | zh_TW |
dc.subject.keyword | ECG,compression,irregular,period,sorting, | en |
dc.relation.page | 85 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2006-06-17 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
顯示於系所單位: | 電機工程學系 |
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