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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 管傑雄(Chieh-Hsiung Kuan) | |
dc.contributor.author | Che-Wei Lee | en |
dc.contributor.author | 李哲瑋 | zh_TW |
dc.date.accessioned | 2021-06-17T03:39:37Z | - |
dc.date.available | 2018-03-02 | |
dc.date.copyright | 2018-03-02 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-02-08 | |
dc.identifier.citation | [1] http://big5.ce.cn/gate/big5/civ.ce.cn/main/gd/201005/21/t20100521_21430096.sht ml
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Eng., vol. 51, no. 4, pp. 582–589, Apr. 2004. [8] Y. H. Hu et al., “Applications of artificial neural networks for ECG signal detection and classification,” J. Electrocardiol., vol. 26, pp. 66–73, 1994. [9] Y. Hu et al., “A patient-adaptable ECG beat classifier using a mixture of experts approach,” IEEE Trans. Biomed. Eng., vol. 44, no. 9, pp. 891–900, Sep. 1997. [10] Ayman Rabee and Imad Barhumi, “ECG classification using support vector machine based on wavelet multiresolution analysis” The 11th International Conference on Information Sciences, Signal Processing and their Applications: Special Sessions, 2016 [11] S. C. Lee, “Using a translation-invariant neural network to diagnose heart arrhythmia,” in Proc. IEEE Conf. Neural Inf. Process. Syst., Nov. 1989, pp. 2025–2026. [12] P. de Chazal and R.B.Reilly, “A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features,” IEEE Trans. Biomed. Eng., vol. 53, no. 12, pp. 2535–2543, Dec. 2006. [13] Recommended Practice for Testing and Reporting Performance Results of Ventricular Arrhythmia Detection Algorithms. Arlington, VA, USA: Association for the. Advancement of Medical Instrumentation, 1987. [14] P. de Chazal et al., “Automatic classification of heartbeats using ECG morphology and heartbeat interval features,” IEEE Trans. Biomed. Eng., vol. 51, no. 7, pp. 1196–1206, Jul. 2004. [15] W. Jiang and S. G. Kong, “Block-based neural networks for personalized ECG signal classification,” IEEE Trans. Neural Networks, vol. 18, no. 6, pp. 1750–1761, Nov. 2007. [16] T. Ince et al., “A generic and robust system for automated patient-specific classification of electrocardiogram signals,” IEEE Trans. Biomed. Eng., vol. 56, no. 5, pp. 1415–1426, May 2009. [17] S. Kiranyaz et al., Multi-Dimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition. New York, NY, USA: Springer, Aug. 2013. [18] M. Llamedo and J. P. Martinez, “An automatic patient-adapted ECG heartbeat classifier allowing expert assistance,” IEEE Trans. Biomed. Eng., vol. 59, no. 8, pp. 2312–2320, Aug. 2012. [19] C. Li et al., “Detection of ECG characteristic points using wavelet transforms,” IEEE Trans. Biomed. Eng., vol. 42, no. 1, pp. 21–28, Jan. 1995. [20] T. Mar et al., “Optimization of ECG classification by means of feature selection,” IEEE Trans. Biomed. Eng., vol. 58, no. 8, pp. 2168–2177, Aug. 2011. [21] R. Mark and G. Moody. MIT-BIH Arrhythmia Database Directory. [Online]. Available: http://ecg.mit.edu/dbinfo.html [22] D. C. Ciresan et al., “Deep big simple neural nets for handwritten digit recognition,” Neural Comput., vol. 22, no. 12, pp. 3207–3220, 2010. [23] D. Scherer et al., “Evaluation of pooling operations in convolutional architectures for object recognition,” in Proc. Int. Conf. Artif. Neural Netw., 2010, pp. 92–101. [24] A. Krizhevsky et al., “Imagenet classification with deep convolutional neural networks,” in Proc. Adv. Neural Inf. Process. Syst. Conf., 2012, pp. 1106–1114. [25] Serkan Kiranyaz∗, Turker Ince, and Moncef Gabbouj, Fellow, IEEE “Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 63, NO. 3, MARCH 2016 [26] 高醫醫訊,第十九卷,第十期,八十九年三月,氣喘專刊. [27] http://eng.mmh.org.tw/taitam/pedia/encyclopedia/book5-10.html [28] A.V Oppenheim and R.W Schafer Pearson, “Discrete-time Signal Processing”, 2010, 3rd edition [29] S. Mallat, “Multifrequency channel decompositions of images and wavelet models,” IEEE Trans. Acoust. Signal Processing, vol. 37, pp.2091–2110, Dec. 1989 [30]Juan Pablo Martínez*, Rute Almeida, Salvador Olmos, Member, IEEE, Ana Paula Rocha, andPablo Laguna, Member, IEEE,” A Wavelet-Based ECG Delineator: Evaluation on Standard Databases”, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 51, NO. 4, APRIL 2004 [31] https://www.zybuluo.com/hanbingtao/note/476663 [32] http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML17_2.html [33] https://www.zybuluo.com/hanbingtao/note/485480 [34] Diederik P. Kingma, University of Amsterdam, Jimmy Lei Ba,University of Toronto ,”ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION”, ICLR 2015 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70028 | - |
dc.description.abstract | 目標:此篇碩士論文提出一個快速、準確,並可以利用很小的訓練資料量,針對不同病患做參數調整的心電圖分類系統。
方法:本研究架出一種可以適應病患的1-D卷積神經網路,只要神經網路訓練完成,輸入的信號不需做任何前處理,便可單獨使用此模型準確達到心電圖分類最大的兩個流程:特徵提取和分類原則。1-D卷積神經網路能用一個共同資料庫及病患本身,針對每一個病患的心電圖進行訓練,經過訓練的神經網路不僅能大幅提高分類準確率,還能省去手刻特徵的過程。此模型不需大量資料即可訓練完成,藉此設計出架構很小的模型,便可應用於心電圖即時診斷的穿戴式裝置。 結果:此模型的結果利用MIT-BIH心律不整資料庫進行驗證分析。結果比絕大部分最先進的方式好或是比其他利用神經網路的方式計算量少很多。 結論:當一個神經網路訓練完成,使用者即可以運用其處理很長的心電圖訊號,不需要再重新訓練;此方式不僅運算量低,且準確率高,此外,更能針對不同病患之心電圖差異進行調節,應用在不同的資料庫上。 | zh_TW |
dc.description.abstract | Goal: This thesis presents a fast and accurate patient specific electrocardiogram (ECG) classification and monitoring system with relatively small training data size.
Methods: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of ECG classification without signal de-noising: feature extraction and classification. Therefore, for each patient, and individual and simple CNN will be trained by using a small common set and a patient specific data, this patient specific feature extraction method can further improve the classification performance. Since this also negates the necessity to extract any hand-crafted manual features, once a dedicated CNN is trained for a specific patient, it can solely be used to classify long ECG data streams in a fast and accurate manner. Using this method the structure of the CNN can be small, allowing this solution to be implemented for real-time ECG monitoring and early alert systems on wearable devices. Results: The results are evaluated over the MIT-BIH arrhythmia data benchmark database, the proposed solution achieves a superior classification performance than most of the state-of-the-art methods, or have much lower computation complexity than other NN solutions for the detection of ventricular ectopic beats and supraventricular ectopic beats. Conclusion: Once a dedicated CNN is trained, it can be used to classify long data streams for an individual patient with extremely low computation complexity with high accuracy. In addition, due to its simple and parameter invariant nature, the proposed system is extremely generic, thus applicable to any ECG dataset. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T03:39:37Z (GMT). No. of bitstreams: 1 ntu-107-R04943020-1.pdf: 1821080 bytes, checksum: d22cf6de22f220cbfbf58642512c4412 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 口試委員審定書 b
致謝 c 摘要 d Abstract e 目錄 i 圖目錄 III 表目錄 IV 第一章 概論 1 1.1.前言 1 1.2研究動機 2 1.3章節概要 4 第二章 研究背景 5 2.1心律不整簡介 5 2.1.1生理結構 5 2.1.2 心電圖簡介 7 2.1.3心電圖的量測 10 2.1.4心律不整的種類 12 2.2 資料庫簡介 14 2.2.1 MIT-BIH資料庫 14 2.2.2 MIT-BIH資料格式 16 2.2.3 AAMI 協會標準 19 2.3 信號處理及機器學習基礎理論 19 2.3.1 重新取樣 19 2.3.2 小波轉換 21 2.3.3 卷積神經網路 23 2.4 程式環境簡介 27 2.4.1 硬體規格 27 2.4.2 程式執行 27 第三章 研究方法 29 3.1 心電圖的信號處理 29 3.1.1 心電圖的訓練流程 29 3.1.2 QRS波峰偵測 31 3.2 卷積模型 34 第四章 結果與討論 37 4.1 分類表現比較 37 4.2 模型訓練過程 39 4.3 系統複雜度 40 第五章 結論與未來展望 41 5.1結論 41 5.2未來研究方向 41 第六章 參考文獻 42 | |
dc.language.iso | zh-TW | |
dc.title | 利用1維卷積神經網及切割法可即時適應病患的心電圖分類 | zh_TW |
dc.title | Real-Time Patient-Specific ECG classification by 1-D Convolutional Neural Networks and Delineation | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林宗男(Tsungnan Lin),孫允武(Yuen-Wuu Suen),陳啟東(Chii-Dong Chen),孫建文(Kien-Wen Sun) | |
dc.subject.keyword | 1-D卷積神經網路,適應病患心電圖分類,ECG切割技術,即時心電圖運算, | zh_TW |
dc.subject.keyword | Convolutional neural networks (CNNs),Patient specific ECG classification,ECG delineation,Real-time heart monitoring, | en |
dc.relation.page | 44 | |
dc.identifier.doi | 10.6342/NTU201800357 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-02-08 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
顯示於系所單位: | 電子工程學研究所 |
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