Signal Extraction in Multisensor Biomedical Recordings

Publisher Summary A wide variety of biomedical problems require the extraction of signals of interest from recordings corrupted by other physiological activity signals, noise, and interference. The task is facilitated when multiple sensors on different locations record simultaneously the biomedical phenomenon under examination. This chapter provides an overview of signal processing techniques to extract the signal(s) of interest and cancel the interference by exploiting the spatial diversity available in the multisensor measurements. It focuses on three different approaches, namely, multi-reference optimal Wiener filtering, spatio-temporal cancellation, and blind source separation. It discusses the working assumptions and main algorithmic implementations of these techniques, and demonstrates their usefulness in real recordings issued from various biomedical signal processing problems. The extraction of signals of interest from measurements corrupted by noise and interference is a fundamental problem arising in a wide variety of domains. In biomedicine, a successful signal extraction can help the physician diagnose and understand a pathologic condition. Exploiting the spatial dimension brought about by multisensor measurements gives rise to the concept of spatial filtering, and enables powerful signal processing approaches with capabilities reaching far beyond conventional single-channel frequency filters. Specific methods depend on the particular problem in hand, its model and its constraints.

[1]  Dinh-Tuan Pham,et al.  Blind separation of instantaneous mixtures of nonstationary sources , 2001, IEEE Trans. Signal Process..

[2]  Wilkin Chau,et al.  Rhythmic brain activities related to singing in humans , 2007, NeuroImage.

[3]  Robert Plonsey,et al.  Bioelectricity: A Quantitative Approach Duke University’s First MOOC , 2013 .

[4]  George-Othon Glentis,et al.  Efficient least squares adaptive algorithms for FIR transversal filtering , 1999, IEEE Signal Process. Mag..

[5]  P. Robinson,et al.  Multiscale brain modelling , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[6]  David Moratal,et al.  Estimation of atrial fibrillatory wave from single-lead atrial fibrillation electrocardiograms using principal component analysis concepts , 2005, Medical and Biological Engineering and Computing.

[7]  Ignace Lemahieu,et al.  Removing Ocular Movement Artefacts by a Joint Smoothened Subspace Estimator , 2007, Comput. Intell. Neurosci..

[8]  P. Comon,et al.  Ica: a potential tool for bci systems , 2008, IEEE Signal Processing Magazine.

[9]  Nikos D. Sidiropoulos,et al.  Parallel factor analysis in sensor array processing , 2000, IEEE Trans. Signal Process..

[10]  Philippe Garat,et al.  Blind separation of mixture of independent sources through a quasi-maximum likelihood approach , 1997, IEEE Trans. Signal Process..

[11]  Bart Vanrumste,et al.  Review on solving the forward problem in EEG source analysis , 2007, Journal of NeuroEngineering and Rehabilitation.

[12]  Philip Langley,et al.  Comparison of atrial signal extraction algorithms in 12-lead ECGs with atrial fibrillation , 2006, IEEE Transactions on Biomedical Engineering.

[13]  B. Widrow,et al.  Adaptive noise cancelling: Principles and applications , 1975 .

[14]  Driss Aboutajdine,et al.  Source separation contrasts using a reference signal , 2004, IEEE Signal Processing Letters.

[15]  Pierre Comon,et al.  Blind identification of under-determined mixtures based on the characteristic function , 2006, Signal Process..

[16]  Mike E. Davies,et al.  Space-Time ICA and EM Brain Signals , 2007, ICA.

[17]  P. McCullagh Tensor Methods in Statistics , 1987 .

[18]  Asoke K. Nandi,et al.  Noninvasive fetal electrocardiogram extraction: blind separation versus adaptive noise cancellation , 2001, IEEE Transactions on Biomedical Engineering.

[19]  Carlos J. Escudero,et al.  A blind signal separation method for multiuser communications , 1997, IEEE Trans. Signal Process..

[20]  Nathalie Delfosse,et al.  Adaptive blind separation of independent sources: A deflation approach , 1995, Signal Process..

[21]  Paul Ferrari,et al.  Consistency of interictal and ictal onset localization using magnetoencephalography in patients with partial epilepsy. , 2003, Journal of neurosurgery.

[22]  D. Lehmann,et al.  Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[23]  Leif Sörnmo,et al.  Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation , 2001, IEEE Transactions on Biomedical Engineering.

[24]  Rémi Gribonval Sparse decomposition of stereo signals with Matching Pursuit and application to blind separation of more than two sources from a stereo mixture , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[25]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[26]  P. Langley,et al.  Frequency analysis of atrial fibrillation , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[27]  C. W. Hesse,et al.  The FastICA algorithm with spatial constraints , 2005, IEEE Signal Processing Letters.

[28]  Patrick Berg,et al.  Artifact Correction of the Ongoing EEG Using Spatial Filters Based on Artifact and Brain Signal Topographies , 2002, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[29]  E. Oja,et al.  Independent Component Analysis , 2013 .

[30]  Wei Lu,et al.  Approach and applications of constrained ICA , 2005, IEEE Transactions on Neural Networks.

[31]  José Millet-Roig,et al.  Spatiotemporal blind source separation approach to atrial activity estimation in atrial tachyarrhythmias , 2005, IEEE Transactions on Biomedical Engineering.

[32]  Michael Zibulevsky,et al.  Underdetermined blind source separation using sparse representations , 2001, Signal Process..

[33]  Heather Ting Ma,et al.  Effects of the physiological parameters on the signal-to-noise ratio of single myoelectric channel , 2007, Journal of NeuroEngineering and Rehabilitation.

[34]  Jean-François Cardoso,et al.  Multidimensional independent component analysis , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[35]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[36]  Gene H. Golub,et al.  Matrix computations , 1983 .

[37]  Pablo Laguna,et al.  Principal Component Analysis in ECG Signal Processing , 2007, EURASIP J. Adv. Signal Process..

[38]  Pierre Comon,et al.  An error bound for a noise canceller , 1989, IEEE Trans. Acoust. Speech Signal Process..

[39]  P. Comon,et al.  Blind Identification of Overcomplete MixturEs of sources (BIOME) , 2004 .

[40]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[41]  Leif Sörnmo,et al.  Characterization of atrial fibrillation using the surface ECG: time-dependent spectral properties , 2001, IEEE Transactions on Biomedical Engineering.

[42]  G Beltramello Coregistration of EEG and fMRI in rolandic epilepsy with evoked spikes by peripheral tapping stimulation , 1997 .

[43]  P. Lorrain,et al.  Electromagnetism: Principles and Applications , 1979 .

[44]  Antoine Chevreuil,et al.  Blind source separation of a mixture of communication sources emitting at various baud-rates , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[45]  Ronald Phlypo,et al.  Extracting common spectral features by multichannel filtering using circulant matrices , 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

[46]  Joos Vandewalle,et al.  Fetal electrocardiogram extraction by blind source subspace separation , 2000, IEEE Transactions on Biomedical Engineering.

[47]  M. Kendall,et al.  Kendall's advanced theory of statistics , 1995 .

[48]  José Millet-Roig,et al.  Atrial activity extraction for atrial fibrillation analysis using blind source separation , 2004, IEEE Transactions on Biomedical Engineering.

[49]  Alan Connelly,et al.  EEG‐fMRI in Children with Pharmacoresistant Focal Epilepsy , 2007, Epilepsia.

[50]  E. So Integration of EEG, MRI, and SPECT in Localizing the Seizure Focus for Epilepsy Surgery , 2000, Epilepsia.

[51]  Shin Ishii,et al.  Markov and Semi-Markov Switching of Source Appearances for Nonstationary Independent Component Analysis , 2007, IEEE Transactions on Neural Networks.

[52]  S Swiryn,et al.  Computer detection of atrioventricular dissociation from surface electrocardiograms during wide QRS complex tachycardias. , 1985, Circulation.

[53]  O. Meste,et al.  QRST cancellation using bayesian estimation for the auricular fibrillation analysis , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[54]  O. Meste,et al.  Atrio-Ventricular Junction behaviour during Atrial Fibrillation , 2007, 2007 Computers in Cardiology.

[55]  Rangaraj M. Rangayyan,et al.  Biomedical Signal Analysis: A Case-Study Approach , 2001 .

[56]  Sabine Van Huffel,et al.  Special issue on linear algebra in signal and image processing - Preface , 2004 .

[57]  Jean-Francois Cardoso,et al.  Blind signal separation: statistical principles , 1998, Proc. IEEE.

[58]  Jie Lian,et al.  Computer modeling of ventricular rhythm during atrial fibrillation and ventricular pacing , 2006, IEEE Transactions on Biomedical Engineering.

[59]  Fetsje Bijma,et al.  The coupled dipole model: an integrated model for multiple MEG/EEG data sets , 2004, NeuroImage.