Recent Advances in Biomedical Signal Processing


by

Juan M. Górriz, Elmar W. Lang , Javier Ramírez

DOI: 10.2174/97816080521891110101
eISBN: 978-1-60805-218-9, 2011
ISBN: 978-1-60805-570-8



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Indexed in: Scopus

Biomedical signal processing is a rapidly expanding field with a wide range of applications, from the construction of artificial limbs...[view complete introduction]

Table of Contents

About the Editors'

- Pp. i

Juan Manuel Górriz, Elmar W. Lang and Javier Ramírez

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Foreword

- Pp. ii

Jose Principe

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Preface

- Pp. iii

Juan Manuel Górriz, Elmar W. Lang and Javier Ramírez

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Contributors

- Pp. iv-xiii (10)

Juan Manuel Górriz, Elmar W. Lang and Javier Ramírez

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Decomposition Techniques In Neuroscience

- Pp. 1-25 (25)

M. De Vos, Lieven De Lathauwer and S. Van Huffel

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Exploratory Matrix Factorization Techniques for Large Scale Biomedical Data Sets

- Pp. 26-47 (22)

E.W. Lang, R. Schachtner, D. Lutter, D. Herold, A. Kodewitz, F. Blochl, F. J. Theis, I. R. Keck, J.M Gorriz Saezd, P. Gomez, P. Gomez Vildae and A. M. Tomec

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Subspace Techniques and Biomedical Time Series Analysis

- Pp. 48-59 (12)

A. M. Tome, A. R. Teixeira and E. W. Lang

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Empirical Mode Decomposition Techniques for Biomedical Time Series Analysis

- Pp. 60-81 (22)

A. Zeiler, R. Faltermeier, M. Bohm, I. R. Keck, A.M. Tome, C. G. Puntonet, A. Brawanski and E.W. Lang

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A Comparison between Univariate and Multivariate Supervised Learning for Classification of SPECT Images

- Pp. 82-94 (13)

F. Segovia, J. M. Gorriz, J. Ramirez, D. Salas-Gonzalez, I. A. Illan, M. Lopez, R. Chaves and P. Padilla

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Functional Brain Image Preprocessing For Computer Aided Diagnosis Systems

- Pp. 95-106 (12)

R. Chaves, D. Salas-Gonzalez, J. Ramirez, J. M. Gorriz, M. Lopez, I. Alvarez and F. Segoviaa

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Functional Image Classification Techniques For Early Alzheimer’s Disease Detection

- Pp. 107-122 (16)

I. Alvarez-Illan, Miriam M. Lopez, J. M. Gorriz, J. Ramirez, F. Segovia, D. Salas-Gonzalez, R. Chaves and C. G. Puntonet

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Time-Frequency Analysis of MEG activity in Alzheimer’s Disease

- Pp. 123-140 (18)

J. Poza and R. Hornero

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Machine Learning Approach for Myotonic Dystrophy Diagnostic Support from MRI

- Pp. 141-148 (8)

Alexandre Savio, Maite Garcia-Sebastian, Andone Sistiaga, Darya Chyzhyk, Esther Fernandez, Fermin Moreno, Elsa Fernandez, Manuel Grana, Jorge Villanua and Adolfo Lopez de Munain

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Finding Gold in The Dirt - Biomedical Artifacts in The Light of ICA

- Pp. 149-156 (8)

I. R. Keck, V. Fischer, C. G. Puntonet, A. M.Tome and E. W. Lang

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Are We to Integrate Previous Information into Microarray Analyses? Interpretation of a Lmx1b-Knockout Experiment

- Pp. 157-170 (14)

Florian Blochl, Anne Rascle, Jurgen Kastner, Ralph Witzgall, Elmar W. Lang and Fabian J. Theis

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Mixed Effects Models for Single-Trial ERP Detection in Noninvasive Brain Computer Interface Design

- Pp. 171-180 (10)

Yonghong Huang, Deniz Erdogmus, Kenneth Hild II, Misha Pavel and Santosh Mathan

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Learning Sparse Similarity Functions for Heart Wall Motion Abnormality Detection

- Pp. 181-190 (10)

Glenn Fung

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Using Spatial Diversity in the Estimation of Atrial Fibrillatory Activity from the Electrocardiogram

- Pp. 191-215 (25)

R. Phlypo, P. Bonizzi, O. Meste and V. Zarzoso

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Ultrasound Image Analysis. Methods and Applications

- Pp. 216-230 (15)

J. Marti, A. Gubern-Merida, J. Massich, A. Oliver, J. C. Vilanova, J. Comet, E. Perez, M. Arzoz and R. Marti

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Reconstruction and Analysis of Intravascular Ultrasound Sequences

- Pp. 231-250 (20)

Francesco Ciompi, Carlo Gatta, Oriol Pujol, Oriol Rodriguez-Leor, Josepa Mauri Ferre and Petia Radeva

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Human Body Position Monitoring

- Pp. 251-268 (18)

Alberto Olivares, J. M. Gorriz, J. Ramirez and Gonzalo Olivares

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Index

- Pp. 269-271 (3)

Juan M. Gorriz, Elmar W. Lang and Javier Ramirez

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Foreword

Our colleagues working in the communications area have luxuries that biomedical engineers cannot afford. In fact, communication engineers build their own systems and signals from scratch and obviously create them based on well known statistical signal processing theories and benefitting from the simplifying assumptions of Gaussianity, linearity and stationarity. However, the signals created by biological organisms defeat these assumptions and one is left with the challenging task to quantify and extract information from amazingly complex signal structures. For many years, biomedical signal processing fell in love with the simple FFT and the linear model, but it is clear that it is leaving now this “local minimum” in search of many more interesting, and more realistic approaches.

This book clearly demonstrates the point exceedingly well. It is a great example that biomedical signal processing continues to be an expanding field, full of challenges but also of innovations and the excitement of “climbing the Everest” that is unparalleled in other areas. The book is composed of 15 chapters that cover a large spectrum of topics but are centered on the problems of decomposing biological signals and images into elements that carry biological meaning for clinical diagnostic.

The paper by Tome et al is a great introduction because it addresses the principles of subspace decompositions for univariate signals. The paper by Blochl et al addresses the issue of interpretation, based on prior knowledge, microarray structures under the very difficult case of large dimension and small number of noisy samples. The paper by Zeiler et al explores the Hilbert Huang transform to discriminate between modes of nonlinear coupled systems. The paper by Segovia et al extends the statistical parametric mapping with a Gaussian mixture model to attack the difficult issue of multivariate modeling in SPECT for Alzheimer’s disease diagnostic. Chavez et al exploits an ordered subset expectation maximization algorithm to improve resolution of SPECT without the processing penalty normally encountered in more conventional techniques. Alvarez-Illan validates feature extraction and classification algorithms to automatically process functional brain images for more reliable diagnostics. Savio et al is also interested in automated diagnostic in MRIs and validates the combination of a voxel based morphometry preprocessor with support vector machines. De Vos et al present a principled view of multivariate decompositions for neuroscience and compare the state of the art techniques in EEG analysis in epilepsy. Lang et al expands on this topic with exploratory matrix factorization for multivariate biological data using novel tensor decomposition methods that are very efficient and promising for microarray data. Keck et al uses ICA for denoising biomedical data and presents very interesting cases of success for this difficult and unsolved problem. Huang et al addresses the very difficult problem of single event related potential detection for brain computer interfaces using generative models of the data to construct a Fisher kernel for support vector machines classification. Fung applies the same basic line of reasoning but now applied to left ventricular wall motion collected with ultrasound to create a Fisher kernel and measure in the corresponding reproducing kernel Hilbert space the distance between normal and coronary heart disease. Phlypo et al use spatial diversity to quantify atrial fibrillation and explains in detail the different methods of implementing spatial filters for diagnostic of this common condition. Marti et al explains the difficulties and the remedies of designing robust ultra sound image segmentation methodologies for freehand data collection, tumor detection and multimodality registration. Ciompi et al explains how atherosclerosis can be reconstructed and analyzed with intravascular ultrasound providing an automated way of diagnostic for this common condition.

I hope you enjoy the reading as much as I have.

Jose Principe, Ph.D.
Gainesville, March 2010


Preface

Few years ago, processing of biomedical signals was mainly concentrating on filtering of signals for removing noise and power lines interference; spectral analysis to understand the frequency characteristics of signals; and modeling for feature representation and parameterization. Biomedical signal processing is a rapidly expanding field with a wide range of applications, from the construction of artificial limbs and aids for disabilities to the development of sophisticated medical imaging systems. These include ultrasound scanners, magnetic resonance imaging scanners and positron emission tomography (PET and SPECT). X-ray systems have also improved and are widely used for many purposes such as mammograms.

On the other hand, acquisition and processing of biomedical signals has become more and more important to the physician. The main reasons for this development are the growing complexity of the biomedical examinations, the increasing necessity of comprehensive documentation and the need for automation in order to reduce costs. Recent trends have been toward quantitative or objective analysis of physiological systems via signal analysis. Analysis of signals accomplished by humans has many limitations, therefore, computer analysis of these signals could provide objective strength to diagnoses; however, the development of an algorithm for biomedical signal analysis is a significant challenge. Different techniques can be used to analyze a biomedical signal, these include: filtering, adaptive noise cancellation, and pattern recognition (to differentiate between abnormal and normal physiological signals). Medical image processing, using techniques such as X-ray and MRI, can be viewed as multi-dimensional signal processing.

In addition, medical practitioners are increasingly using computer-based medical systems to collect, store, and process digitized biological signals. The signals they collect and process via these systems include bioelectric potentials, such as those generated by the heart (ECG or electrocardiogram), brain (EEG or electroencephalogram), or skeletal muscles (EMG or electro-myogram). Such signals include nonelectric signals that might be transduced and then recorded (for example, breath sounds or speech waveforms), and biomedical images from ultrasound, Xray, AT scan, MRI, SPECT, PET, etc. These signals are often interpreted heuristically by medical practitioners, and the need for sophisticated algorithms for processing, coding, and automatically interpreting the information these signals contain is increasing. Among the advantages of automated processing are objectivity, reliability, repeatability, and speed. Of course, as biomedical signal processing algorithms gain sophistication, more computational resources are needed, while most biomedical signal processing must be done in or near real time. The volume of data is often high, computational resources are often scarce, and the cost of resources and computing time is important. Thus, it is necessary to simultaneously consider not only whether a particular technique is useful but also how it might be efficiently mapped to special-purpose hardware to be included in a computer-based medical system. The transition from uni-processor to multiprocessor architectures is mandatory to achieve real-time performance in computerbased medical systems for biomedical signal and image processing.

This e-BOOK will cover biomedical signal processing as used in both therapeutic and diagnostic instrumentation. A number of current research projects will also be outlined with emphasis on intelligent medical image diagnosis. We would like to express our gratitude to all the contributing authors that have made a reality this book. We would like to also thank Dr. Principe for writing the foreword and Bentham Science Publishers, particularly Manager Sara Moqeet, for their support and efforts.

Juan M. Górriz, Javier Ramírez and Elmar W. Lang

University of Granada, Spain University of Regensburg, Germany

List of Contributors

Editor(s):
Juan M. Górriz
University of Granada
Spain


Elmar W. Lang
University of Regensburg
Germany


Javier Ramírez
University of Granada
Spain




Contributor(s):
M. Arzoz
University Hospital “Germans Trias i Pujol”
Badalona, 08916
Spain


Florian Blöchl
CMB Group, Bioinformatics and Systems Biology
Helmholtz Center Munich
Germany


P. Bonizzi
I3S Lab - UNS/CNRS, Les Algorithmes
Sophia Antipolis CEDEX, Bat. Euclide B 2000
Route des Lucioles B.P. 121 06903
Badalona
France


Matthias Böhm
Computational Intelligence and Machine Learning (CIML) Group
Institute of Biophysics, University of Regensburg
Regensburg, D-93040
Germany


Alexander Brawanski
Neurosurgery
University Medical Centre
Regensburg, D-93040
Germany


R. Chaves
Dpto Teoría de la Señal, Telemática y Comunicaciones
Universidad de Granada
BadGranadaalona, E-18071
Spain


Darya Chyzhyk
Computational Intelligence Group
University of Basque Country
Paseo Manuel de Lardizabal 1, San
Sebastian, 20018
Spain


Francesco Ciompi

Universitat de Barcelona
Gran Via de les Corts Catalanes 585
Barcelona, 08007
Spain


J. Comet

University Hospital “Dr. Josep Trueta”
Girona, 17007
Spain


Marteen De Vos
Campus Kortrijk Group Science, Engineering and Technology
Katholieke Universiteit Leuven
Kortrijk, 8500
Belgium


Lieven De Lathauwer
Campus Kortrijk, Group Science, Engineering and Technology
Katholieke Universiteit Leuven
Kortrijk, 8500
Belgium


Deniz Erdogmus
Cognitive Systems Lab, Electrical and Computer Engineering Department
Northeastern University
Boston
MA
USA


Rupert Faltermeier
Neurosurgery
University Medical Centre
Regensburg, D-93040
Germany


Elsa Fernández
Computational Intelligence Group
University of Basque Country
Paseo Manuel de Lardizabal 1
San Sebastian, 20018
Spain


Esther Fernández
Osatek, Hospital Donostia
Paseo Dr.Beguiristain 109
San Sebastian, 20018
Spain


Volker Fischer

Institute for Experimental Psychology
University of Regensburg
Germany


Glenn Fung
IKM CKS, Siemens Healthcare
Malvern
PA, 08916
USA


Carlos Garcia Puntonet
Depto. Arquitectura y Tecnología de Computadores
Universidad de Granada
Granada, E-18071
Spain


Maite García-Sebastián
Computational Intelligence Group
University of Basque Country
Paseo Manuel de Lardizabal 1
San Sebastian, 20018
Spain


Carlo Gatta
Computer Vision Center
Edifici O, Campus UAB, 08193 Bellaterra
Barcelona, 08916
Spain


Pedro Gómez Vilda
Computational Intelligence Group
University of Basque Country
Paseo Manuel de Lardizabal 1
San Sebastian, 20018
Spain


Maite García-Sebastián
Depto. de Arquitectura y Tecnología de Sistemas Informáticos
Universidad Politécnica de Madrid
Madrid, E-18500
Spain


J. M. Górriz-Sáez
Dpto Teoría de la Señal, Telemática y Comunicaciones
Universidad de Granada
Granada, E-18071
Spain


Manuel Graña
Computational Intelligence Group
University of Basque Country
Paseo Manuel de Lardizabal 1
San Sebastian, 20018
Spain


A. Gubern-Mérida
Computer Vision and Robotics Group
Universitat de Girona
Girona, 17071
Spain


Daniela Herold
Computational Intelligence and Machine Learning (CIML) Group
Institute of Biophysics, University of Regensburg
Regensburg, D-93040
Germany


Kenneth Hild II
Augmented Cognition Lab, Division of Biomedical Engineering
Oregon Health and Science University
Portland
OR
USA


R. Hornero
Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación
University of Valladolid
Spain


Yonghong Huang
Augmented Cognition Lab, Division of Biomedical Engineering
Oregon Health and Science University
Portland
OR
USA


I. A. Illán
Dpto Teoría de la Señal, Telemática y Comunicaciones
Universidad de Granada
Granada, E-18071
Spain


Jürgen Kastner
Institut für Anatomie
Universität Regensburg
San Sebastian
Germany


Ingo R. Keck
Computational Intelligence and Machine Learning (CIML) Group
Institute of Biophysics, University of Regensburg
Paseo Manuel de Lardizabal 1
Regensburg, D-93040
Germany


Andreas Kodewitz
Computational Intelligence and Machine Learning (CIML) Group
Institute of Biophysics, University of Regensburg
Paseo Manuel de Lardizabal 1
Regensburg, D-93040
Germany


Elmar W. Lang
Computational Intelligence and Machine Learning (CIML) Group
Institute of Biophysics, University of Regensburg
Paseo Manuel de Lardizabal 1
Regensburg, D-93040
Germany


Adolfo López de Munain
Hospital Donostia – Unidad Experimental
Edificio Materno Infantil (-3 azul)
Pº Dr Begiristain s/n
San Sebastian, 20014
Spain


Dominik Lutter
Computational Intelligence and Machine Learning (CIML) Group
Institute of Biophysics, University of Regensburg
Paseo Manuel de Lardizabal 1
Regensburg, D-93040
Germany


J. Martí
Computer Vision and Robotics Group
Universitat de Girona
Girona, 17071
Spain


R. García-Sebastián
Computer Vision and Robotics Group
Universitat de Girona
Girona, 17071
Spain


J. Massich
Computer Vision and Robotics Group
Universitat de Girona
Girona, 17071
Spain


Santosh Mathan
Human Centered Systems Lab
Honeywell Research Laboratories
Seattle
WA
USA


Josepa Mauri Ferré
Hospital Universitari Germans Trias i Pujol
Carretera de Canyet s/n,
Badalona, 08916
Spain


O. Meste
I3S Lab - UNS/CNRS
Les Algorithmes, Bat. Euclide B 2000
Route des Lucioles B.P. 121
Sophia Antipolis CEDEX, 06903
France


Fermín Moreno
Hospital Donostia – Unidad Experimental
Edificio Materno Infantil (-3 azul), Pº Dr Begiristain s/n
San Sebastian, 20014
Spain


A. Oliver
Computer Vision and Robotics Group
Universitat de Girona
Girona, 17071
Spain


Alberto Olivares
Dpt. Computer Architecture and Technology
University of Granada, ETSIIT-UGR
San Sebastian, 18071
Spain


Gonzalo Olivares
Dpt. Computer Architecture and Technology
University of Granada, ETSIIT-UGR
San Sebastian, 18071
Spain


Alberto Olivares
Dpt. Computer Architecture and Technology
University of Granada, ETSIIT-UGR
Spain


Gonzalo Olivares
Dpt. Computer Architecture and Technology
University of Granada, ETSIIT-UGR
Spain


P. Padilla
Dpto Teoría de la Señal, Telemática y Comunicaciones
Universidad de Granada
Granada, E-18071
Spain


Misha Pavel
Augmented Cognition Lab, Division of Biomedical Engineering
Oregon Health and Science University
Portland
OR, E-18071
USA


E. Pérez
University Hospital “Dr. Josep Trueta”
Girona, 17007
Spain


R. Phlypo
I3S Lab - UNS/CNRS, Les Algorithmes
Bat. Euclide B 2000, Route des Lucioles B.P. 121
Sophia Antipolis CEDEX, 06903
France


J. Poza
Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación
University of Valladolid
Spain


Oriol Pujol
Universitat de Barcelona
Gran Via de les Corts Catalanes 585
Barcelona, 08007
Spain


Petia Radeva
Universitat de Barcelona
Gran Via de les Corts Catalanes 585
Barcelona, 08007
Spain


J. Ramírez
Dpto Teoría de la Señal, Telemática y Comunicaciones
Universidad de Granada
Granada, E-18071
Spain


Anne Rascle
Institut für Anatomie, Universität Regensburg
Germany


Oriol Rodríguez-Leor
Hospital Universitari Germans Trias i Pujol
Carretera de Canyet s/n,
Badalona, 08916
Spain


D. Salas-González
Dpto Teoría de la Señal, Telemática y Comunicaciones
Universidad de Granada
Granada, E-18071
Spain


Alexandre Savio
Computational Intelligence Group
University of Basque Country
Paseo Manuel de Lardizabal 1
San Sebastián, 20018
Spain


Reinhard Schachtner
Computational Intelligence and Machine Learning (CIML) Group
Institute of Biophysics, University of Regensburg
Regensburg, D-93040
Germany


F. Segovia
Dpto Teoría de la Señal, Telemática y Comunicaciones
Universidad de Granada
Granada, E-18071
Spain


Andone Sistiaga
Departamento de Neurociencias
University of Basque Country
Campus Leioa de la UPV/EHU
Spain


Ana Rita Teixeira
Instituto de Engenharia Electrónica e Telemática
Universidade de Aveiro
Aveiro, P-3810-193
Portugal


Andone Sistiaga
Departamento de Neurociencias
University of Basque Country
Campus Leioa de la UPV/EHU
Spain


Fabian J. Theis
CMB Group, Bioinformatics and Systems Biology
Helmholtz Center Munich
Germany


Ana Maria Tomé
Instituto de Engenharia Electrónica e Telemática
Universidade de Aveiro
Aveiro, P-3810-193
Portugal


Sabine Van Huffel
Campus Kortrijk Group Science, Engineering and Technology
Katholieke Universiteit Leuven
Kortrijk, 8500
Belgium


J.C. Vilanova
Girona Magnetic Resonance Center
Girona, 17002
Spain


Jorge Villanúa
Osatek, Hospital Donostia
Paseo Dr.Beguiristain 109
San Sebastián, 20014
Spain


Ralph Witzgall
Institut für Anatomie, Universität Regensburg
Germany


V. Zarzoso
I3S Lab - UNS/CNRS, Les Algorithmes
Bat. Euclide B 2000, Route des Lucioles B.P. 121
Sophia Antipolis CEDEX, 06903
France


Angela Zeiler
Computational Intelligence and Machine Learning (CIML) Group
Institute of Biophysics, University of Regensburg
Regensburg, D-93040
Germany




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