摘要(英) |
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.. |
參考文獻 |
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. |