Title page for 955201093


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Student Number 955201093
Author Wei-Hsuan Wu(吳維軒)
Author's Email Address knola1122@yahoo.com.tw
Statistics This thesis had been viewed 1724 times. Download 1025 times.
Department Electrical Engineering
Year 2007
Semester 2
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title The measurement and application of Independent Component Analysis in instant ECG
Date of Defense 2008-07-05
Page Count 67
Keyword
  • Electrocardiogram (ECG)
  • Electromyography (EMG)
  • Independent Component Analysis(ICA)
  • Abstract With the developments of advanced medical instruments in recent years, the remote medicine and homecare system have been recognized as a new trend in the interaction between patients and doctors. This trend changes the life style of care medicine. Patients can use advanced nursing systems to record their physiological data at home and transmit these data to hospital network for necessarily monitoring. Nevertheless, these achievements require the novel developments of medical instruments, especially the noise-proof performance of these instruments.
      In this study, we aim to develop an Independent Component Analysis (ICA)-based ECG care system. ICA is a multi-variable technique which has been validated as a powerful tool for separating different signals according to their distinct statistical distributions. With the benefit of ICA, physiological and environmental ECG-unrelated noise can be removed so that the ECG signals can be extracted in low signal-to-noise (SNR) situation, even during uses’s limb movements. In order to validate the performance of the proposed ICA-based system, we attached six ECG electrodes (three on left hand and the other three on right hand) to extract the surface ECG of a user. ECG-unrelated noise and physiological signals, such as 60 Hz electricity noise, low frequency drifts and electromyogram contaminations can be identified and removed. Currently, we have implemented the ICA-based ECG care system on Labview platform for real-time processing. Further developments are required to realize the technique using dsPIC microprocessor for portable homecare purposes..
    Table of Content 目錄
    中文摘要I
    AbstractII
    致謝IV
    目錄V
    圖目錄VIII
    表目錄XIII
    第一章 緒論1
    1.1前言1
    1.2相關研究於生理訊號文獻回顧2
    1.3研究動機2
    1.4研究目的3
    1.5研究方法3
    1.6論文架構4
    第二章 心電圖原理介紹5
    2.1生理訊號簡介5
    2.2心電圖簡介5
    2.2.1心電圖來源6
    2.2.2向量與導程的概念7
    2.2.3十二導程7
    2.3心電圖功能9
    第三章 研究理論與方法10
    3.1 引言10
    3.2 獨立成份分析法原理簡介10
    3.3 訊號原理簡介13
    3.3.1 峰態(kurtosis)14
    3.3.2 熵(entropy)15
    3.4 獨立成份分析法演算流程17
    3.4.1 第一部分:資料的前置處理18
    3.4.2 第二部份: Fixed-point algorithm using kurtosis20
    3.4.3 第三部份:重建來源訊號的大小24
    第四章 實驗原理與流程25
    4.1實驗說明25
    4.2 實驗架構26
    4.2.1硬體架構26
    4.2.2軟體架構28
    第五章 實驗結果35
    5.1模擬混合訊號的分離35
    5.2實驗前言37
    5.2.1即時訊號分離系統-身體放鬆38
    5.2.2即時訊號分離系統-手臂動作42
    5.2.3即時訊號分離系統-手臂用力46
    5.2.4即時訊號分離系統-電力線雜訊增強51
    5.2.5即時訊號分離系統-走路55
    5.2.6即時訊號分離系統-肢體碰觸59
    第六章 結論64
    參考文獻66
    Reference 參考文獻
    [1].T .W. Lee, Independent Component Analysis: Theory and Applications, Kluwer Academic Publishers, Boston, MA, 1998.
    [2].行政院衛生署國民健康局,。http://www.bhp.doh.gov.tw/BHPnet/Portal/.
    [3].A. Hyvärinen, J. Karhunen and E. Oja, Independent Component Analysis, John Wiley & Sons, Inc., New York, 2001.
    [4].R. Vigário, J. Särelä, V. Jousmäki, M. Hämäläinen, and E. Oja, “Independent Component Approach to the Analysis of EEG and MEG Recording,” IEEE Trans. Biomed. Eng., vol. 47, pp.589-593, 2000.
    [5].Koredianto Usman et al, “A Study of Heartbeat Sound Separation Using Independent Component Analysis Technique”, IEEE 6th International Workshop on
    [6].L. De Lathauwer, B. De Moor, and J. Vandewalle, “Fetal Electrocardio- gram Extraction by Blind Source Subspace Separation,” IEEE Trans. Biomed. Eng., vol. 47, pp.567-572, 2000.
    [7].馬偕紀念院, http//www.mmh.org.tw/taitam/csc/doc/ekgbasic.htm.
    [8].Frank G. Yanowitz, M.D.,1997. http://www.pharmacology2000.com/Cardio/Cardio_risk/adult_cardiac_procedures/anatomy3.htm.
    [9].李玉菁 何杏棻等人,人體解剖學,文京圖書有限公司,1996。
    [10].A. Hyvarinene and E. Oja, “Independent Component Analysis: Algorithms and Applications Neural Networks”, vol. 13, pp. 411-430, 2000.
    [11].T. W. Lee, M. S. Lewicki and T. J. Sejnowski, “ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, pp.1078-1089, 2000.
    [12].Roy D. Yates and David J. Goodman, PROBILITY AND STOCHASTIC PROCESS, John Wiley & Sons, Inc., New York, 1999.
    [13].生訊科技股份有限公司, http://www.bios ensetek.com/Cindex.html.
    [14].12導心電圖機, ensetek.com/productECG.html.
    [15].http://search.ni.com/nise arch/main/p?q=USB+6259.
    [16].NI USB-6259, http://sine.ni.com/nips/cds/view/p/lang/en/nid/202598.
    Advisor
  • Po-Lei Lee(李柏磊)
  • Files
  • 955201093.pdf
  • approve in 2 years
    Date of Submission 2008-07-19

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