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Techniques and Applications of the Elimination of the Cardiac Contribution in MEG Measurements

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Computational Methods in Biophysics, Biomaterials, Biotechnology and Medical Systems
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5.1 5.1 Introduction

Magnetoencephalography (MEG) [18] deals with the detection and interpretation of the minute magnetic fields (50–500 fTesla) generated by electrical activity in the brain. An array of SQUID detectors (Super-conducting QUantum Interference Devices) [27] is placed near the cortical generators and records the brain activity for a short period of time, usually for 1 or 2 s. These recordings are called epochs. The sources of the MEG signal are the same as the ones generating the electrical surface potential on the scalp recorded by the more familiar electroencephalogram (EEG). The MEG and EEG signals are generated directly by the electrical activity in the brain. Typical MEG and/or EEG signals show features lasting from tenths of a millisecond to a few milliseconds. This implies that it is unlikely that the major contributor to the signal is the action potential propagation in the axons of neurons. It is generally agreed that the generators are ionic flows in the...

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References

  • K. Abraham-Fuchs, P. Strobach, W. Harer and S. Schneider Improvement of neuromagnetic localisation by MCG artifact correction in MEG recordings. 8th Int. Conf. Biomag., Munster, Aug 19–24, pp. 787–791, 1991.

    Google Scholar 

  • N. Ahmed and K.R. Rao. Orthogonal Transforms for Digital Signal Processing. Springer-Verlag, Berlin Heidelberg, 1975.

    Google Scholar 

  • A. Angelidou, M.G. Strintzis, S. Panas and G. Anogianakis. On AR modelling for MEG spectral estimation, data compression and classification. Comput. Biol. Med. 22(6): 379–387, 1992.

    Article  Google Scholar 

  • J.I. Auon, C.D. McGillem and D.G. Childers. Signal processing in evoked potential research: averaging and modelling. CRC Crit. Rev. Bioeng. 5: 323–367, 1981.

    Google Scholar 

  • R. Bloch. Subtraction of electrocardiographic signal from respiratory electromyogram. Journal of Applied Physiology 55(2): 619–623, 1983.

    Google Scholar 

  • J.D. Bronzino (Ed.), The Biomedical Engineering Handbook. CRC Press in cooperation with IEEE Press, 1995.

    Google Scholar 

  • D. Burstein and E. Weinstein. Some relations between the various criteria for autoregressive order determination. IEEE Trans. Acoust. Speech Signal Processing 26: 1017–1019, 1981.

    Google Scholar 

  • D. Callaerts, J. Vanderschoot and W. Sansen. An adaptive on-line method for the extraction of the complete FECG from abdominal multilead recordings. J. Perinat. Med. 14: 421–433, 1986.

    Article  Google Scholar 

  • A.A. Damen and J. Van Der Kam. The use of singular value decomposition in electrocardiography. Med. Biol. Eng. Comput. 20: 473–482, 1982.

    Article  Google Scholar 

  • H. Fan and X. Liu. Delta Levinson and Schur-Type RLS algorithms for adaptive signal processing. IEEE Trans. Signal Processing 42(7): 1629–1639, 1994.

    Article  Google Scholar 

  • J. Franden and M.R. Neuman. QRS wave detection. Med. Biol. Eng. Comput. 18: 125–132, 1980.

    Article  Google Scholar 

  • G.M. Friesen, T.C. Jannett, M.A. Jadallah, S.L. Yates, S.R. Quint and H.T. Nagle. A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Trans. Biomed. Eng. 37(1): 85–98, 1990.

    Article  Google Scholar 

  • G.H. Golub and C.F. van Loan. Matrix Computations. The Johns Hopkins University Press, Baltimore, 2nd edition, 1989.

    MATH  Google Scholar 

  • T. Gansler and M. Hansson. Estimation of the single event potential wave shape. Proc. of the 15th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, pp. 438–439. San Diego, USA, October 28–31, 1993.

    Google Scholar 

  • A.S. Gevins. Analysis of the electromagnetic signals of the human brain: milestones, obstacles and goals. IEEE Trans. Biomed. Eng. 31: 833–850, 1984.

    Article  Google Scholar 

  • G.H. Golub and C. Reinsch. Singular value decomposition and least square solutions. Numer. Math. 14: 403–420, 1970.

    Article  MathSciNet  MATH  Google Scholar 

  • A.R. Haig and P.G. Rogers. Eigenspace methods for spatio-temporal analysis of multichannel EEC recordings. In: D. Gray (Ed.), ISSPA 92, Signal Processing and its Applications. Gold Coast, Australia, pp. 433–436, August 16–21, 1992.

    Google Scholar 

  • M. Hamalainen, R. Hari, R.J. Ilmoniemi, J. Knuutila and O.V. Lounasmaa. Megnetoencepfalography-theory, instrumentation and applications to non-invasive studies of the working human brain. Rev. Modern Phys. 65(2): 413–497, 1993.

    Article  Google Scholar 

  • S. Haykin. Adaptive Filter Theory. 2nd edition, Prentice-Hall, 1991.

    Google Scholar 

  • A.A. Ioannides. Estimates of brain activity using magnetic field tomography and large scale communication within the brain. In: M.W. Ho, F.A. Popp and U. Warnke (Eds), Bioelectrodynamics and Biocommunication, pp. 319–353. World Scientific, Singapore, 1994.

    Google Scholar 

  • V.K. Iyer, P.A. Ramamoorthy, H. Fan and Y. Ploysongsang. Reduction of heart sounds from lung sounds by adaptive filtering. IEEE Trans. Biomed. Eng. 33(12): 1141–1148, 1986.

    Article  Google Scholar 

  • R. Jane, H. Rix, P. Caminal and P. Laguna. Alignment methods for averaging of high-resolution cardiac signals: a comparative study of performance. IEEE Trans. Biomed. Eng. 38(6): 571–579, 1991.

    Article  Google Scholar 

  • R. Kalman and R. Bucy. New results in linear filtering and prediction theory. Trans. ASME, Ser. D. J. Basic Eng. 83: 95–107, 1961.

    Article  MathSciNet  Google Scholar 

  • T. Kailath. A view of three decades of linear filtering theory. IEEE Trans. Inf. Theory. 20: 145–181, 1974.

    Article  Google Scholar 

  • P. Laguna, R. Jane, O. Meste, P.W. Poon, P. Caminal, H. Rix and N.V. Thakor. Adaptive filters for event related bioelectric signals using an impulsive correlated reference input: comparison with signal averaging techniques. IEEE Trans. Biomed. Eng. 41(8): 792–800, 1992.

    Google Scholar 

  • P.S. Lewis. Adaptive enhancement of magnetoencephalographic signals via multichannel filtering. Proc. of the Int. Conf. on Acoustic Speech and Signal Processing, pp. 1512–1515. IEEE, Glasgow, Scotland, May 1989.

    Google Scholar 

  • O.V. Lounasmaa. Experimental Principles and Methods Below 1 K. Academic Press, London, 1974.

    Google Scholar 

  • B. Lutkenhoner, M. Hoke and C. Pantev. Possibilities and limitations of weighted averaging. Biolog. Cybernet. 52: 409–416, 1985.

    Article  Google Scholar 

  • J.C. Mocher, P.S. Lewis and R. Leahy. Subspace methods for identifying neural activity from electromagnetic measurements of the brain. Invited Paper in Proc. of the 25th Asilomar Conf. on Signal, Systems and Computers, pp. 237–241. IEEE, Pacific Grove, CA, November 1991.

    Google Scholar 

  • A. van Oosterom and J. Alsters. Removing the maternal component in the fetal ECG using singular value decomposition. In: Rutttkay-Nedecky and P. MacFarlane (Eds), Electrocardiology 83, pp. 171–176. Amsterdam, The Netherlands: Ex cerpta Medica, 1984.

    Google Scholar 

  • S.J. Orfanidis. Optimum Signal Processing: An Introduction, 2nd edition. McGRAW-HILL, 1988.

    Google Scholar 

  • J. Pan and W.J. Tompkins. A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3): 230–236, 1985.

    Article  Google Scholar 

  • V. Parsa and P.A. Parker. Multireference adaptive noise cancellation applied to somatosensory evoked potentials. IEEE Trans. Biomed. Eng. 41(8): 792–800, 1994.

    Article  Google Scholar 

  • W.H. Press, S.A. Teukolsky, W.T. Vetterling and B.P. Flannery. Numerical Recipes in C. The Art of Scientific Computing. 2nd edition. Cambridge University Press, 1992.

    Google Scholar 

  • L.J. Rogers and R.R. Douglas. A new statistical method for resolving superimposed signals. In: Stroink and Gehrard (Eds), Biomagnetism: Applications and Theory. Pergamon Press, Great Britain, 1985.

    Google Scholar 

  • G.L. Romani. Fundamentals of neuromagnetism. In: S.J. Williamson, M. Hoke, G. Stroink and M. Kotani (Eds), Advances in Biomagnetism. Plenum Press, New York, pp. 587–590, 1989.

    Google Scholar 

  • M. Samonas. Pre-processing of magneto-encephalographic signals. Ph.D. Thesis, University of Surrey, UK, 1996.

    Google Scholar 

  • M. Samonas, M. Petrou and A. Ioannides. Identification and elimination of cardiac contribution in single trial MagnetoEncephaloGraphic signals. IEEE Trans. Biomed. Eng. 44(5): 386–393, 1997.

    Article  Google Scholar 

  • T.W. Schweitzer, J.W. Fitzgerald, J.A. Bowden and P. Lynn-Davies. Spectral analysis of human inspiratory diaphragmatic electromyograms. J. Appl. Physiol. 46: 152–165, 1979.

    Google Scholar 

  • P. Strobach. Linear prediction theory: a mathematical basis for adaptive systems. Springer Series in Information Sciences, Vol. 21. Berlin, Germany, 1990.

    Google Scholar 

  • P. Strobach. New forms of Levinson and Schur algorithms. IEEE Signal Processing Mag. 8(1): 12–36, 1991.

    Article  Google Scholar 

  • P. Strobach, K. Abraham-Fuchs and W. Harer. Event-synchronous cancellation of the heart interference in biomedical signals. IEEE Trans. Biomed. Eng. 41(4): 343–350, 1994.

    Article  Google Scholar 

  • A. Suzuki, C. Sumi, K. Nakayama and M. Mori. Real-time adaptive cancellation of ambient noise in lung sound measurement. Med. Biologi. Eng. Comp. 33: 704–708, 1995.

    Article  Google Scholar 

  • C.D. Tesche, M.A. Uusitalo, R.J. Ilmoniemi, M. Huotilainen, M. Kajola and O. Salonem. Signal-space projections of MEG data characterise both distributed and well-localised neuronal sources. Electroencephalogr. Clin. Neurophysiol. 95(3): 189–200, 1995.

    Article  Google Scholar 

  • N.V. Thakor, J.G. Webster and W.J. Tompkins. Estimation of QRS complex power spectra for design of a QRS filter. IEEE Trans. Biomed. Eng. 31(11): 702–706, 1984.

    Article  Google Scholar 

  • P.E. Trahanias. An approach to QRS complex detection using mathematical morphology. IEEE Trans. Biomed. Eng. 40(2): 201–205, 1993.

    Article  Google Scholar 

  • J. Vanderschoot, J. Vandewalle, J. Janssens et al. Extraction of weak bioelectrical signals by means of singular value decomposition. In: A. Bensoussan and J.L. Lions (Eds), Analysis and Optimisation of Systems, Lecture Notes in Control and Information Sciences 63. pp. 334–348. Springer-Verlag, Berlin, Germany, 1984.

    Google Scholar 

  • J. Vanderschoot, D. Callaerts, W. Sansen, J. Vandewalle, G. Vantrappen and J. Janssens. Two methods for optimal MECG elimination and FECG detection from skin electrode signals. IEEE Trans. Biomed. Eng. 34(3): 233–242, 1987.

    Article  Google Scholar 

  • J. Vandewalle, J. Vanderschoot and B.De Moor. Source separation by adaptive SVD. Proc. ISCAS, pp. 1351–1354, 1985.

    Google Scholar 

  • R. Vautard, P. Yiou and M. Ghil. Singular-spectrum analysis: a toolkit for short, noisy chaotic signals. Physica D, 58: 95–126, 1992.

    Article  Google Scholar 

  • A.J. van der Veen, E.F. Deprettere and A.L. Swindlehurst. Subspace-based signal analysis using singular value decomposition. Proc. IEEE, 81(9): 1277–1308, 1993.

    Article  Google Scholar 

  • J. Vrba, B. Taylor, T. Cheung, A.A. Fife, G. Haid, P.R. Kubic, S. Lee, J. McCubbin and M.B. Burbank. Noise cancellation by a whole-cortex squid MEG system. IEEE Trans. Appl. Superconductiv. 5(2): 2118–2123, 1995.

    Article  Google Scholar 

  • J.P. C. de Weerd. A posteriori time-varying filtering of averaged evoked potentials. Biolog. Cybernet. 41: 211–222, 1981.

    Article  MATH  Google Scholar 

  • N. Wiener. Extrapolation, Interpolation and Smoothing of Stationary Time Series, with Engineering Applications. John Wiley, New York, 1949.

    MATH  Google Scholar 

  • B. Widrow, J.R. Glover, J.M. McCool, J. Kaunitz, C.S. Williams, R.H. Hearn, J.R. Zeidler, E. Dong and R.C. Goodlin. Adaptive noise cancelling: principles and applications. Proc. IEEE 63: 1692–1716, December, 1975.

    Article  Google Scholar 

  • B. Widrow and SamuelD. Stearns. Adaptive Signal Processing. Prentice-Hall, 1985.

    Google Scholar 

  • J.C. Woestenburg, M.N. Verbaten and J.L. Slangen. The removal of the eye-movement artifact from the EEG by regression analysis in the frequency domain. Biolog. Psychol. 16: 127–147, 1983.

    Article  Google Scholar 

  • Q. Xue, Y.H. Hu and W.J. Tompkins. Neural network based adaptive matched filtering for QRS detection. IEEE Trans. Biomed. Eng. 39(4): 317–329, 1992.

    Article  Google Scholar 

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Cornelius T. Leondes

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Leondes, C.T. (2003). Techniques and Applications of the Elimination of the Cardiac Contribution in MEG Measurements. In: Leondes, C.T. (eds) Computational Methods in Biophysics, Biomaterials, Biotechnology and Medical Systems. Springer, Boston, MA. https://doi.org/10.1007/0-306-48329-7_5

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  • DOI: https://doi.org/10.1007/0-306-48329-7_5

  • Publisher Name: Springer, Boston, MA

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