Chapter 18 – Biomedical applications

Publisher Summary This chapter focuses on the use of independent component analysis (ICA) in biomedical systems. ICA method is employed for identification and removal of the reference signal contribution from intracranial electro-encephalography (iEEG) recorded with a scalp reference signal. All EEG potential measurements reflect the difference between two potentials, and can then be factorized as a static mixture of two source subspaces corresponding to the reference signal and signals of interest, respectively. The former subspace is one dimensional by definition, while the latter subspace may have a higher dimension. As far as the statistical independence between both subspaces is concerned, it should at least be approximately true because of the high resistivity of the skull between scalp electrode and intracranial electrodes. Then the FastICA method is used in order to automatically extract the scalp reference signal based on its independence from the bipolar iEEG data. When iEEG and scalp EEG are recorded simultaneously using the same scalp reference, the scalp reference signal R2 extracted by ICA from iEEG can also be used in order to clean the scalp EEG.

[1]  Cédric Févotte,et al.  Two contributions to blind source separation using time-frequency distributions , 2004, IEEE Signal Processing Letters.

[2]  Lotfi Senhadji,et al.  Blind source separation for ambulatory sleep recording , 2006, IEEE Transactions on Information Technology in Biomedicine.

[3]  Lucas C. Parra,et al.  Nonnegative matrix factorization for rapid recovery of constituent spectra in magnetic resonance chemical shift imaging of the brain , 2004, IEEE Transactions on Medical Imaging.

[4]  Asoke K. Nandi,et al.  Blind separation of independent sources for virtually any source probability density function , 1999, IEEE Trans. Signal Process..

[5]  M. Junghöfer,et al.  The polar average reference effect: a bias in estimating the head surface integral in EEG recording , 1999, Clinical Neurophysiology.

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

[7]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[8]  Armando Malanda,et al.  Independent Component Analysis as a Tool to Eliminate Artifacts in EEG: A Quantitative Study , 2003, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[9]  John J. Shynk,et al.  A successive cancellation algorithm for fetal heart-rate estimation using an intrauterine ECG signal , 2002, IEEE Trans. Biomed. Eng..

[10]  C. Joyce,et al.  Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. , 2004, Psychophysiology.

[11]  Joos Vandewalle,et al.  Independent component analysis and (simultaneous) third-order tensor diagonalization , 2001, IEEE Trans. Signal Process..

[12]  M. I. Owis,et al.  Characterisation of electrocardiogram signals based on blind source separation , 2002, Medical and Biological Engineering and Computing.

[13]  Laurent Albera,et al.  Fourth-order blind identification of underdetermined mixtures of sources (FOBIUM) , 2005, IEEE Transactions on Signal Processing.

[14]  Ben H. Jansen,et al.  Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns , 1995, Biological Cybernetics.

[15]  S Makeig,et al.  Blind separation of auditory event-related brain responses into independent components. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Sanqing Hu,et al.  Automatic Identification and Removal of Scalp Reference Signal for Intracranial EEGs Based on Independent Component Analysis , 2007, IEEE Transactions on Biomedical Engineering.

[17]  Jean-Louis Lacoume,et al.  Statistics for complex variables and signals - Part I: Variables , 1996, Signal Process..

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

[19]  T. Sejnowski,et al.  Analysis and visualization of single‐trial event‐related potentials , 2001, Human brain mapping.

[20]  Pascal Chevalier,et al.  Optimal separation of independent narrow-band sources: Concept and performance , 1999, Signal Process..

[21]  Erkki Oja,et al.  Independent component approach to the analysis of EEG and MEG recordings , 2000, IEEE Transactions on Biomedical Engineering.

[22]  A. Engel,et al.  What is novel in the novelty oddball paradigm? Functional significance of the novelty P3 event-related potential as revealed by independent component analysis. , 2005, Brain research. Cognitive brain research.

[23]  J. Westgate,et al.  Do Fetal Electrocardiogram PR-RR Changes Reflect Progressive Asphyxia after Repeated Umbilical Cord Occlusion in Fetal Sheep? , 1998, Pediatric Research.

[24]  Guy Carrault,et al.  Atrial activity enhancement by Wiener filtering using an artificial neural network , 2001, IEEE Transactions on Biomedical Engineering.

[25]  Barak A. Pearlmutter,et al.  Independent Components of Magnetoencephalography: Localization , 2002, Neural Computation.

[26]  J. Pulkkinen,et al.  Independent component analysis to proton spectroscopic imaging data of human brain tumours. , 2005, European journal of radiology.

[27]  Jean-Marc Vesin,et al.  Observer of autonomic cardiac outflow based on blind source separation of ECG parameters , 2000, IEEE Transactions on Biomedical Engineering.

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

[29]  V. Fuster,et al.  ACC/AHA/ESC Guidelines for the Management of Patients With Atrial Fibrillation: Executive Summary A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the European Society of Cardiology Committee for Practice Guidelines and Policy Conference , 2001, Circulation.

[30]  R N Vigário,et al.  Extraction of ocular artefacts from EEG using independent component analysis. , 1997, Electroencephalography and clinical neurophysiology.

[31]  M. Basseville.  Distance measures for signal processing and pattern recognition , 1989 .

[32]  H. K. Verma,et al.  RETRACTED: Artifacts and noise removal in electrocardiograms using independent component analysis , 2008 .

[33]  Richard M. Leahy,et al.  Source localization using recursively applied and projected (RAP) MUSIC , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[34]  José Luis Pérez Velazquez,et al.  Phase synchronization measurements using electroencephalographic recordings , 2007, Neuroinformatics.

[35]  Richard M. Leahy,et al.  Electromagnetic brain mapping , 2001, IEEE Signal Process. Mag..

[36]  Antoine Souloumiac,et al.  Jacobi Angles for Simultaneous Diagonalization , 1996, SIAM J. Matrix Anal. Appl..

[37]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[38]  Dario Farina,et al.  Blind separation of linear instantaneous mixtures of nonstationary surface myoelectric signals , 2004, IEEE Transactions on Biomedical Engineering.

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

[40]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[41]  N.V. Thakor,et al.  Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection , 1991, IEEE Transactions on Biomedical Engineering.

[42]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[43]  Tony F. Chan,et al.  An Improved Algorithm for Computing the Singular Value Decomposition , 1982, TOMS.

[44]  L. Faes,et al.  Principal component analysis and cluster analysis for measuring the local organisation of human atrial fibrillation , 2001, Medical and Biological Engineering and Computing.

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

[46]  Lionel Tarassenko,et al.  Application of independent component analysis in removing artefacts from the electrocardiogram , 2006, Neural Computing & Applications.

[47]  Lieven De Lathauwer,et al.  Blind Identification of Underdetermined Mixtures by Simultaneous Matrix Diagonalization , 2008, IEEE Transactions on Signal Processing.

[48]  S. Lévy,et al.  Atrial fibrillation: current knowledge and recommendations for management. Working Group on Arrhythmias of the European Society of Cardiology. , 1998, European heart journal.

[49]  Jan A. Kors,et al.  Adaptive Gaussian filtering in routine ECG/VCG analysis , 1986, IEEE Trans. Acoust. Speech Signal Process..

[50]  Visa Koivunen,et al.  Blind separation methods based on Pearson system and its extensions , 2002, Signal Process..

[51]  Arnaud Delorme,et al.  Frontal midline EEG dynamics during working memory , 2005, NeuroImage.

[52]  E. Oja,et al.  BSS and ICA in Neuroinformatics: From Current Practices to Open Challenges , 2008, IEEE Reviews in Biomedical Engineering.

[53]  Laurent Albera,et al.  Brain Source Localization Using a Fourth-Order Deflation Scheme , 2008, IEEE Transactions on Biomedical Engineering.

[54]  Herbert Bauer,et al.  Using ICA for removal of ocular artifacts in EEG recorded from blind subjects , 2005, Neural Networks.

[55]  Christian Jutten,et al.  On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics , 2008, Clinical Neurophysiology.

[56]  Allan Kardec Barros,et al.  Removing artifacts from electrocardiographic signals using independent components analysis , 1998, Neurocomputing.

[57]  S. Baillet,et al.  Automated interictal spike detection and source localization in magnetoencephalography using independent components analysis and spatio-temporal clustering , 2004, Clinical Neurophysiology.

[58]  M. Chung.  Current clinical issues in atrial fibrillation. , 2003, Cleveland Clinic journal of medicine.

[59]  Barak A. Pearlmutter,et al.  Independent Components of Magnetoencephalography: Single-Trial Response Onset Times , 2002, NeuroImage.

[60]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.

[61]  Arie Yeredor,et al.  Non-orthogonal joint diagonalization in the least-squares sense with application in blind source separation , 2002, IEEE Trans. Signal Process..

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

[63]  D. Cohen.  Magnetoencephalography: Detection of the Brain's Electrical Activity with a Superconducting Magnetometer , 1972, Science.

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

[65]  T. Sejnowski,et al.  Independent component analysis of fMRI data: Examining the assumptions , 1998, Human brain mapping.

[66]  Christopher J. James,et al.  Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis , 2003, IEEE Transactions on Biomedical Engineering.

[67]  Jean-Louis Lacoume,et al.  Statistics for complex variables and signals - Part II: signals , 1996, Signal Process..