Advanced Methods of Biomedical Signal Processing E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: IEEE Press Series on Biomedical Engineering
• Sprache: Englisch

Seitenzahl: 960

Veröffentlichungsjahr: 2011

Beschreibung

This book grew out of the IEEE-EMBS Summer Schools on Biomedical Signal Processing, which have been held annually since 2002 to provide the participants state-of-the-art knowledge on emerging areas in biomedical engineering. Prominent experts in the areas of biomedical signal processing, biomedical data treatment, medicine, signal processing, system biology, and applied physiology introduce novel techniques and algorithms as well as their clinical or physiological applications. The book provides an overview of a compelling group of advanced biomedical signal processing techniques, such as multisource and multiscale integration of information for physiology and clinical decision; the impact of advanced methods of signal processing in cardiology and neurology; the integration of signal processing methods with a modelling approach; complexity measurement from biomedical signals; higher order analysis in biomedical signals; advanced methods of signal and data processing in genomics and proteomics; and classification and parameter enhancement.

Leseprobe

CONTENTS

COVER

HALF TITLE PAGE

TITLE PAGE

COPYRIGHT PAGE

PREFACE

REFERENCES TO PREFACE

CONTRIBUTORS

PART I: FUNDAMENTALS OF BIOMEDICAL SIGNAL PROCESSING AND INTRODUCTION TO ADVANCED METHODS

CHAPTER 1: METHODS OF BIOMEDICAL SIGNAL PROCESSING: Multiparametric and Multidisciplinary Integration toward a Better Comprehension of Pathophysiological Mechanisms

1.1 INTRODUCTION

1.2 FUNDAMENTAL CHARACTERISTICS OF BIOMEDICAL SIGNALS AND TRADITIONAL PROCESSING APPROACHES

1.3 LINK BETWEEN PHYSIOLOGICAL MODELING AND BIOMEDICAL SIGNAL PROCESSING

1.4 THE PARADIGM OF MAXIMUM SIGNAL-SYSTEM INTEGRATION

1.5 CONCLUSIONS

REFERENCES

CHAPTER 2: DATA, SIGNALS, AND INFORMATION: Medical Applications of Digital Signal Processing

2.1 INTRODUCTION

2.2 CHARACTERISTIC ASPECTS OF BIOMEDICAL SIGNAL PROCESSING

2.3 UTILITY AND QUALITY OF APPLICATIONS

2.4 GRAPHIC METHODS FOR INTERACTIVELY DETERMINING THE MOST DISCRIMINANT ORIGINAL VARIABLES

2.5 ALARM GENERATION

APPENDIX

REFERENCES

PART II: POINTS OF VIEW OF THE PHYSIOLOGIST AND CLINICIAN

CHAPTER 3: METHODS AND NEURONS

3.1 WHAT IS AN OBJECT?

3.2 WHICH OBJECT PROPERTY IS DEFINITELY INTERESTING?

3.3 ARE THERE BEST TECHNIQUES?

3.4 ADAPTEDNESS OF TECHNIQUES

BIBLIOGRAPHY

CHAPTER 4: EVALUATION OF THE AUTONOMIC NERVOUS SYSTEM: From Algorithms to Clinical Practice

4.1 INTRODUCTION

4.2 RELATIONSHIP BETWEEN HEART RATE VARIABILITY AND MYOCARDIAL INFARCTION

4.3 RELATIONSHIP BETWEEN HEART RATE VARIABILITY AND HEART FAILURE

4.4 RELATIONSHIP BETWEEN HEART RATE AND BLOOD PRESSURE VARIABILITY

4.5 SUDDEN DEATH RISK STRATIFICATION, PROPHYLACTIC TREATMENT, AND UNRESOLVED ISSUES

4.6 THE ROLE OF AUTONOMIC MARKERS IN NONINVASIVE RISK STRATIFICATION

REFERENCES

PART III: MODELS AND BIOMEDICAL SIGNALS

CHAPTER 5: PARAMETRIC MODELS FOR THE ANALYSIS OF INTERACTIONS IN BIOMEDICAL SIGNALS

5.1 INTRODUCTION

5.2 BRIEF REVIEW OF OPEN-LOOP IDENTIFICATION

5.3 CLOSED-LOOP IDENTIFICATION

5.4 APPLICATIONS TO CARDIOVASCULAR CONTROL

5.5 NONLINEAR INTERACTIONS AND SYNCHRONIZATION

5.6 CONCLUSION

REFERENCES

CHAPTER 6: USE OF INTERPRETATIVE MODELS IN BIOLOGICAL SIGNAL PROCESSING

6.1 INTRODUCTION

6.2 MATHEMATICAL INSTRUMENTS FOR SIGNAL PROCESSING

6.3 EXAMPLES

6.4 CONCLUSIONS

REFERENCES

CHAPTER 7: MULTIMODAL INTEGRATION OF EEG, MEG, AND FUNCTIONAL MRI IN THE STUDY OF HUMAN BRAIN ACTIVITY

7.1 INTRODUCTION

7.2 CORTICAL ACTIVITY ESTIMATION FROM NONINVASIVE EEG AND MEG MEASUREMENTS

7.3 INTEGRATION OF EEG/MEG AND fMRI DATA

APPENDIX I. ELECTRICAL FORWARD SOLUTION FOR A REALISTIC HEAD MODEL

APPENDIX II. MAGNETIC FORWARD SOLUTION

REFERENCES

CHAPTER 8: DECONVOLUTION FOR PHYSIOLOGICAL SIGNAL ANALYSIS

8.1. INTRODUCTION

8.2 DIFFICULTIES OF THE DECONVOLUTION PROBLEM

8.3 THE REGULARIZATION METHOD

8.4 OTHER DECONVOLUTION METHODS

8.5 A STOCHASTIC NONLINEAR METHOD FOR CONSTRAINED PROBLEMS

8.6 CONCLUSIONS AND DEVELOPMENTS

REFERENCES

PART IV: TIME-FREQUENCY, TIME-SCALE, AND WAVELET ANALYSIS

CHAPTER 9: LINEAR TIME-FREQUENCY REPRESENTATION

9.1 INTRODUCTION

9.2 THE SHORT-TIME FOURIER TRANSFORM

9.3 TIME-FREQUENCY RESOLUTION

9.4 MULTIRESOLUTION ANALYSIS

9.5 WAVELET TRANSFORM

9.6 A GENERALIZATION OF THE SHORT-TIME FOURIER TRANSFORM

9.7 WAVELET TRANSFORM AND DISCRETE FILTER BANKS

9.8 MATCHING PURSUIT

9.9 APPLICATIONS TO BIOMEDICAL SIGNALS

9.10 CONCLUSIONS

REFERENCES

CHAPTER 10: QUADRATIC TIME-FREQUENCY REPRESENTATION

10.1 INTRODUCTION

10.2 A ROUTE TO TIME-FREQUENCY REPRESENTATIONS

10.3 WIGNER–VILLE TIME-FREQUENCY REPRESENTATION

10.4 INTERFERENCE TERMS

10.5 COHEN’S CLASS

10.6 PARAMETER QUANTIFICATION

10.7 APPLICATIONS

10.8 CONCLUSIONS

REFERENCES

CHAPTER 11: TIME-VARIANT SPECTRAL ESTIMATION

11.1 INTRODUCTION

11.2 LMS METHODS

11.3 RLS ALGORITHM

11.4 COMPARISON BETWEEN LMS AND RLS METHODS

11.5 DIFFERENT FORMULATIONS OF THE FORGETTING FACTOR

11.6 EXAMPLES AND APPLICATIONS

11.7 EXTENSION TO MULTIVARIATE MODELS

11.8 CONCLUSION

APPENDIX 1. LINEAR PARAMETRIC MODELS

APPENDIX 2. LEAST SQUARES IDENTIFICATION

APPENDIX 3. COMPARISON OF DIFFERENT FORGETTING FACTORS

REFERENCES

PART V: COMPLEXITY ANALYSIS AND NONLINEAR METHODS

CHAPTER 12: DYNAMICAL SYSTEMS AND THEIR BIFURCATIONS

12.1 DYNAMICAL SYSTEMS AND STATE PORTRAITS

12.2 STRUCTURAL STABILITY

12.3 BIFURCATIONS AS COLLISIONS

12.4 LOCAL BIFURCATIONS

12.5 GLOBAL BIFURCATIONS

12.6 CATASTROPHES, HYSTERESIS, AND CUSP

12.7 ROUTES TO CHAOS

12.8 NUMERICAL METHODS AND SOFTWARE PACKAGES

REFERENCES

CHAPTER 13: FRACTAL DIMENSION: From Geometry to Physiology

13.1 GEOMETRY

13.2 FRACTAL OBJECTS

13.3 FRACTALS IN PHYSIOLOGY

13.4 HURST EXPONENT

13.5 CONCLUDING REMARKS

REFERENCES

FURTHER READING

CHAPTER 14: NONLINEAR ANALYSIS OF EXPERIMENTAL TIME SERIES

14.1 INTRODUCTION

14.2 RECONSTRUCTION IN THE EMBEDDING SPACE

14.3 TESTING FOR NONLINEARITY WITH SURROGATE DATA

14.4 ESTIMATION OF INVARIANTS: FRACTAL DIMENSION AND LYAPUNOV EXPONENTS

14.5 DIMENSION OF KAPLAN AND YORKE

14.6 ENTROPY

14.7 NONLINEAR NOISE REDUCTION

14.8 CONCLUSION

APPENDIX

REFERENCES

CHAPTER 15: BLIND SOURCE SEPARATION: Application to Biomedical Signals

15.1 INTRODUCTION

15.2 MATHEMATICAL MODELS OF MIXTURES

15.3 PROCESSING TECHNIQUES

15.4 APPLICATIONS

APPENDIX

ACKNOWLEDGMENTS

REFERENCES

CHAPTER 16: HIGHER ORDER SPECTRA

16.1. INTRODUCTION

16.2. HIGHER ORDER STATISTICS: DEFINITION AND MAIN PROPERTIES

16.3. BISPECTRUM AND BICOHERENCE: DEFINITIONS, PROPERTIES, AND ESTIMATION METHODS

16.4. ANALYSIS OF NONLINEAR SIGNALS: QUADRATIC PHASE COUPLING

16.5. IDENTIFICATION OF LINEAR SYSTEMS

16.6. INTERACTION AMONG CARDIORESPIRATORY SIGNALS

16.7. CLINICAL APPLICATIONS OF HOS: BISPECTRAL INDEX FOR ASSESSMENT OF ANAESTHESIA DEPTH

REFERENCES

PART VI: INFORMATION PROCESSING OF MOLECULAR BIOLOGY DATA

CHAPTER 17: MOLECULAR BIOENGINEERING AND NANOBIOSCIENCE: Data Analysis and Processing Methods

17.1 INTRODUCTION

17.2 DATA ANALYSIS AND PROCESSING METHODS FOR GENOMICS IN THE POSTGENOMIC ERA

17.3 FROM GENOMICS TO PROTEOMICS

17.4 PROTEIN STRUCTURE DETERMINATION

17.5 CONCLUSIONS

REFERENCES

CHAPTER 18: MICROARRAY DATA ANALYSIS: General Concepts, Gene Selection, and Classification

18.1 INTRODUCTION

18.2 FROM MLCROARRRAY TO GENE EXPRESSION DATA

18.3 IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES

18.4 CLASSIFICATION: UNSUPERVISED METHODS

18.5 CLASSIFICATION: SUPERVISED METHODS

18.6 CONCLUSIONS

REFERENCES

INTERNET RESOURCES

CHAPTER 19: MICROARRAY DATA ANALYSIS: Gene Regulatory Networks

19.1 INTRODUCTION

19.2 BOOLEAN MODELS

19.3 DIFFERENTIAL EQUATION MODELS

19.4 BAYESIAN MODELS

19.5 CONCLUSIONS

REFERENCES

CHAPTER 20: BIOMOLECULAR SEQUENCE ANALYSIS

20.1 INTRODUCTION

20.2 CORRELATION IN DNA SEQUENCES

20.3 SPECTRAL METHODS IN GENOMICS

20.4 INFORMATION THEORY

20.5 PROCESSING OF PROTEIN SEQUENCES

BIBLIOGRAPHY

PART VII: CLASSIFICATION AND FEATURE EXTRACTION

CHAPTER 21: SOFT COMPUTING IN SIGNAL AND DATA ANALYSIS: Neural Networks, Neuro-Fuzzy Networks, and Genetic Algorithms

21.1 INTRODUCTION

21.2 ADAPTIVE NETWORKS

21.3 NEURAL NETWORKS

21.4 LEARNING

21.5 STRUCTURAL ADAPTATION

21.6 NEURO-FUZZY NETWORKS

21.7 GENETIC ALGORITHMS

REFERENCES

CHAPTER 22: INTERPRETATION AND CLASSIFICATION OF PATIENT STATUS PATTERNS

22.1 THE CLASSIFICATION PROCESS

22.2 THE BAYES CLASSIFIER

22.3 A DIFFERENT APPROACH TO INTERPRET (AND CLASSIFY) DATA: CLUSTER ANALYSIS

22.4 APPLICATIONS TO BIOMEDICAL DATA

22.5 VISUAL EXPLORATION OF BIOMEDICAL DATA

REFERENCES

INDEX

IEEE Press Series in Biomedical Engineering

ADVANCED METHODS OF BIOMEDICAL SIGNAL PROCESSING

IEEE Press445 Hoes LanePiscataway, NJ 08855

IEEE Press Editorial BoardLajos Hanzo, Editor in Chief

R. AbhariM. El-HawaryO. P. MalikJ. AndersonB-M. Haemmerli        S. NahavandiG. W. Arnold        M. LanzerottiT. SamadF. CanaveroD. JacobsonG. Zobrist

Kenneth Moore, Director of IEEE Book and Information Services (BIS)

This book was previously published in Italian under the title, Metodi avanzati elaborazione dei segnali biomedici, by Sergio Cerutti and Carlo Marchesi. © 2004 Pátron Editore, Bologna, Italy.

Copyright © 2011 by the Institute of Electrical and Electronics Engineers, Inc.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. All rights reserved.

Published simultaneously in Canada.

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ISBN: 978-0-470-42214-4

oBook ISBN: 978-1-118-00774-7ePDF ISBN: 978-1-118-00772-3ePub ISBN: 978-1-118-00773-0

PREFACE

THIS BOOK DEALS with some of the most advanced methodological approaches in signal analysis of biomedical interest. A basic background of digital signal treatment is generally required in order to better cope with the more advanced methods. Readers who are not familiar with the basic concepts of signal (or biomedical signal) processing should read the first two sections, which cover the most important concepts at a level that will be useful for correctly understanding the other chapters. Some basic references are included for those who want to go deep into detail on the fundamentals of signal (or biomedical signal) processing, which is not the aim of the present book.

Today, the evolution of the various approaches employed for biomedical signal processing (as well as the implementation of suitable algorithms of signals and data treatment, sometimes complex and sophisticated) makes difficult the proper choice of the method to be used according to previously defined objectives. Such objectives might consist of simple signal/noise improvement; the extraction of informative parameters that are important from the clinical standpoint; diagnostic classification; patient monitoring; diagnosis or, in critical cases, the surveillance of subjects such as elderly people or neonates, who can encounter acute episodes; as well as the control of determined chronic pathologies. Hence, to know the wide range of methods that could be employed in the various contexts of signals and data processing is certainly a major qualifying issue.

On the other hand, the clinical studies that have successfully employed advanced methods of signal processing are numerous. The sixth most cited paper from Circulation Research (which is perhaps the most prestigious journal dealing with advanced research themes of the cardiovascular system) is one of the fundamental papers about the theme of heart rate variability (HRV) (Pagani et al., 1986). In Medline, about 12,000 papers have been indexed that deal with this theme. More remarkably, for the journal Circulation, the third most cited paper is the well-known Task Force on Heart Rate Variability study (Malik et al., 1996). In these papers, parameters obtained even via nontraditional approaches of signal processing are widely reported and commented upon. These approaches are deterministic or stochastic, linear or nonlinear, monovariate or multivariate, fractal-like, and so on, and important pathophysiological correlates are suggested and documented.

Furthermore, new equipment is available in the clinical market that employs parameters derived not only from traditional signal processing approaches but also characterized by a certain methodological complexity, such as bispectral indexing or the measurement of entropy parameters in EEG signals for the monitoring of anesthesia level (manufactured by Aspect Medical Systems, Inc. and GE-Datex/Ohmeda).

Various contributions in this book will show that biomedical signal processing has to be viewed in a wider context than the one generally attributed to it, with important links to the modeling phase of the signal-generating mechanisms, so as to better comprehend the behavior of the biological system under investigation. The fundamental concept is that often the modeling phase of a biological system and the processing phase of the relevant signals are linked to each other and sometimes reciprocally and synergically contribute to improve the knowledge of the biological system under study.

This book also includes the presentation of signals and data processing methods that are homogenous and, hence, may be used to integrate in the same metric system information derived from different approaches, coming from different biological systems, on different observation scales. In this way, a more integrated and, hence, more holistic, view of data, signal, and image processing might significantly contribute to an effective improvement of the clinical knowledge of a single patient.

If it is true that any biological signal carries information relative to the system or systems that generated it, we can say that the processing of the signal has the following main objectives: (1) to enhance the useful information from the original signal, (2) to interpret the results and to validate the obtained parameters for the following decision phase, and (3) to produce innovation for the improvement of physiological knowledge, the production of new “intelligent” medical equipment and devices, and the definition of new clinical protocols for prevention, diagnosis, and therapy.

This book is a translated and updated version of a textbook written in Italian for a summer school course organized in Bressanone by the Gruppo Nazionale di Bioingegneria (GNB), the Italian Scientific Society on Bioengineering, in 2004 and published by Pátron Editore.

In Part I, some basic elements of the peculiarities of biomedical signal processing in respect to other more traditional applications of digital signal processing and their classification are introduced.

Part II presents an experimental physiologist’s and cardiologist’s view of central nervous system. Both note the importance of the fundamental step of information processing in biomedical signals and data.

Part III illustrates an important link between biomedical signal processing and physiological modeling. Generally, the two approaches are separated; those who do signal processing do not do modeling and vice versa. An integration between the two approaches is required in order to establish a higher level of comprehension of complex pathophysiological phenomena.

Part IV covers time-frequency, time-scale, and wavelet analysis, with which linear, quadratic, and time-varying estimation of parameters are introduced for understanding the dynamical responses of complex physiological systems. The well-known compromise between time and frequency resolutions in time-scale approach is mainly treated and various applications in biomedical systems are described.

Part V deals with advanced methods that are employed in the fascinating area of complexity measurements, from chaotic systems, to fractal geometry of biological systems behavior when described in the space–state domain, to the nonlinear phenomena that might mimic different behaviors and, hence, provide different interpretations of the physics of the biological phenomena under study.

Part VI tackles an original and innovative application field of data processing: the one that operates at the scale of genes and proteins. Computational genomics and proteomics is a growing area of investigation in the so-called postgenomic era. The challenge is to apply well-known methods of digital signal processing at the level of data sequences constituted by the series of bases constituting DNA strings, as well as of elementary amino acids constituting proteins. Noteworthy possibilities of application are foreseen in both data processing and modeling of the biological phenomena involved.

Finally, Part VII describes important methods for signal classification, such as neural networks and neuro-fuzzy and genetic algorithms.

Due to the articulated and differentiated characteristics of biological systems and their reciprocal relationships, the expert in advanced biomedical signal processing might be compared to Plato’s story of a man in a cave. The man is unchained and his sight is forced toward the bottom of the cave. He must guess the nature of reality outside the cave from the pale shadows that real objects project on the bottom of the cave. Analogously, biomedical signals, which are by their nature complex and corrupted by noise from different sources, often provide only a shadow of reality and must be processed in nontrivial ways to be really informative. The good biomedical signal processing expert should try to properly use a priori information from the model of signal-generating mechanisms, their statistical characteristics, and their interactions. In order to improve the capacity of processing, in many instances the entire procedure might include a phase of integration of information on different scales, by considering also many monovariate sensors and parameters contemporaneously, as previously mentioned.

A major issue is represented by the passage from data and parameters obtained from the processing to the real production of information and, hence, to the effective application of medical care. In biomedical applications, it is not true that the more information, the better; one has to be focused on which information is really useful and which is not.

ICT (information and communication technology) instruments and devices are thought to be crucial to properly implement efficient and effective solutions in patients with chronic or acute pathological conditions and elderly and disabled subjects. On the other hand, they may have drawbacks, as has been well documented. Among these, health costs are always increasing in developed countries, due to the need to do various exams more times or in very short time intervals, due to the lack of a link among hospitals and ambulatory systems, as well as adequately distributed storage systems. We are approaching a critical situation as we lack a suitable culture to widely employ numerous low-cost tools with very simplified procedures that are available to end the patient’s isolation and to encourage his participation in self-care. It is worth remembering that among the prioritary research projects at MIT in Boston as well as in EU projects (Sixth and Seventh Framework Programmes), the development of personal and wearable equipment for the detection and treatment of vital signs is included.

The problems caused by the offer of personal technologies in a variety limited only by imagination will bring attention to the topic of the cooperation among different disciplines, among users and service providers, and among researchers and equipment manufacturers. The problems connected with the ergonomics and usability of the devices as well as esthetic, functional, psychological, and environmental issues, will join the technical ones related to the efficient and effective realization of these new personal devices. In this way, the single patient/subject constitutes a node of the world information system.

According to some experts on complex systems, life as a process, not as an attribute, will always find the way to establish itself in any environment and situation, as it will always find how to adapt itself. The anthropological implication of this statement is given by technology itself. We might say that man could be the species that will realize completely life through technology.

SERGIO CERUTTICARLO MARCHESI

Milan, ItalyFlorence, ItalyMarch 2011

REFERENCES TO PREFACE

Malick, M. et al., and Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Heart rate variability—Standards and measurement, physiological interpretation, and clinical use, Circulation, Vol. 93, 1043–1065, 1996.

Pagani, M., Lombardi, F., Guzzetti, S., Rimoldi, O., Furlan, R., Pizzinelli, P., Sandrone, G., Malfatto, G., Dell’Orto, S., Piccaluga, E., Turiel, M., Baselli, G., Cerutti, S., and Malliani, A., Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog, Circulation Research, Vol. 59, No. 2, pp. 178–193, 1986.

CONTRIBUTORS

FABIO BABILONI, Institute of Human Physiology, University of Rome, “La Sapienza,” Rome, Italy

RITA BALOCCHI, Institute of Clinical Physiology, National Research Council (Consiglio Nazionale delle Ricerche, CNR), Pisa, Italy

GIUSEPPE BASELLI, Department of Bioengineering, Polytechnic of Milan, Milan, Italy

RICCARDO BELLAZZI, Department of Computer Engineering and System Science, University of Pavia, Pavia, Italy,

ANNA MARIA BIANCHI, Department of Bioengineering, Polytechnic of Milan, Milan, Italy

SILVIO BICCIATO, Department of Chemical Engineering, University of Padova, Padova, Italy

GABRIELE E. M. BIELLA, Institute of Bioimaging and Molecular Physiology, National Research Council (Consiglio Nazionale delle Ricerche, CNR), Milan, Italy

PAOLO BOLZERN, Department of Electronics and Information, Polytechnic of Milan, Milan, Italy

GIOVANNI CALCAGNINI, Department of Technology and Health, National Institute of Health, Rome, Italy

FEDERICA CENSI, Department of Technology and Health, National Institute of Health, Rome, Italy

SERGIO CERUTTI, Department of Bioengineering, Polytechnic of Milan, Milan, Italy

FEBO CINCOTTI, IRCCS Foundation Santa Lucia, Rome, Italy

CLAUDIO COBELLI, Department of Information Engineering, University of Padova, Padova, Italy

GIUSEPPE DE NICOLAO, Department of Computer Engineering and System Science, University of Pavia, Pavia, Italy,

FABIO DERCOLE, Department of Electronics and Information, Polytechnic of Milan, Milan, Italy

FABRIZIO DE VICO FALLANI, Institute of Human Physiology, University Rome, La Sapienza, Rome, Italy

BARBARA DI CAMILLO, Department of Information Engineering, University of Padova, Padova, Italy

MANUELA FERRARIO, Department of Bioengineering, Polytechnic of Milan, Milan, Italy

FULVIA FERRAZZI, Department of Computer Engineering and Systems Science, University of Pavia, Pavia, Italy

LORIANO GALEOTTI, Department of Systems and Informatics, University of Florence, Florence, Italy

ALEŠ HOLOBAR, Department of Electronics, Polytechnic of Torino, Torino, Italy

MARIA TERESA LA ROVERE, Cardiology Division, IRCCS Foundation, Maugeri, Montescano, Pavia, Italy

FRANCESCO LUNGHI, Department of Computer Engineering and Systems Science, University of Pavia, Pavia, Italy

GIOVANNI MAGENES, Department of Computer Engineering and Systems Science, University of Pavia, Pavia, Italy

PAOLO MAGNI, Department of Computer Engineering and Systems Science, University of Pavia, Pavia, Italy

LUCA T. MAINARDI, Department of Bioengineering, Polytechnic of Milan, Milan, Italy

CARLO MARCHESI, Department of Systems and Informatics, University of Florence, Florence, Italy

ROBERTO MERLETTI, Department of Electronics, Polytechnic of Torino, Torino, Italy

LUCA MESIN, Department of Electronics, Polytechnic of Torino, Torino, Italy

MATTEO PAOLETTI, Department of Systems and Informatics, University of Florence, Florence, Italy

LINDA PATTINI, Department of Bioengineering, Polytechnic of Milan, Milan, Italy

GIANLUIGI PILLONETTO, Department of Information Engineering, University of Padova, Padova, Italy

ALBERTO PORTA, Department of Technologies for Health, Galeazzi Institute, University of Milan, Milan, Italy

STEFANO RAMAT, Department of Computer Engineering and Systems Science, University of Pavia, Pavia, Italy

SERGIO RINALDI, Department of Electronics and Information, Polytechnic of Milan, Milan, Italy

CARMELINA RUGGIERO, Department of Informatics, Systems and Telematics, University of Genoa, Genoa, Italy

LUCIA SACCHI, Department of Computer Engineering and Systems Science, University of Pavia, Pavia, Italy

MARIA GABRIELLA SIGNORINI, Department of Bioengineering, Polytechnic of Milan, Milan, Italy

GIOVANNI SPARACINO, Department of Information Engineering, University of Padova, Padova, Italy

GIANNA TOFFOLO, Department of Information Engineering, University of Padova, Padova, Italy

MAURIZIO VARANINI, Institute of Clinical Physiology, National Research Council (Consiglio Nazionale delle Ricerche, CNR), Pisa, Italy

MAURO URSINO, Department of Electronics and Informatics, University of Bologna, Bologna, Italy

PART I

FUNDAMENTALS OF BIOMEDICAL SIGNAL PROCESSING AND INTRODUCTION TO ADVANCED METHODS

CHAPTER 1

METHODS OF BIOMEDICAL SIGNAL PROCESSING

Multiparametric and Multidisciplinary Integration toward a Better Comprehension of Pathophysiological Mechanisms

Sergio Cerutti

1.1 INTRODUCTION

It is well known that medicine, in both research environments (particularly in physiology) and clinical applications, is becoming a more and more quantitative discipline based upon objective data obtained from the patient or the subject under examination through digital parameters, vital signs and signals, images, statistical and epidemiological indicators, and so on. Today, even clinical applications cannot be made without a more or less extended background of quantitative indicators that complement generally anamnestic parameters as well as those of the typical objective examination. Patients’ medical records therefore contain more and more data, signals, images, indicators of normality, morbidity, sensitivity, and specificity, and other numerical parameters that must be properly integrated in order to help physicians to make correct decisions for diagnostic evaluations as well as in therapeutic interventions.

Actually, proper sensors or transducers are able to make biological measurements at the various parts of the human body, from the usual electrocardiographic (ECG) or electroencephalographic (EEG) signals, arterial blood pressure signals (ABP), respiration, and so on, up to metabolic signals obtainable from a proper processing of functional images from functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and other means. Hence, the information detectable from the patient has grown by considering various organs and systems simultaneously.

Even in biology, mainly due to the very strong acceleration of research in the last few years into the genome and proteome structure sequencing, much importance has been attributed to the methods that allow the information treatment starting from sequences formed by four bases (A, C, G, T) in the genome and twenty amino acids, which are the constituents of the proteins in the proteome. Many of the methods of information treatment, in the form of biomedical signals and data, can be applied to these sequences, after a proper transformation into numerical series; and this is certainly only a single example of the possible important innovative applications of biomedical engineering to molecular biology.

This chapter and the next one illustrate the fundamentals of a modern approach to the processing and interpretation of biomedical signals by exploring the main research lines and some of the many applications that will be examined in more detail in subsequent chapters. Knowledge of these methods, their algorithms, and the techniques used for their realizations can become a sophisticated investigational tool, not only to enhance useful information from the signals (which is the more “traditional” objective of this discipline) but even to approach in a more quantitative way the study of complex biological systems, certainly with a more important impact on the improvement of the physiological knowledge as well as the clinical applications. It is fundamental to remember that the more advanced methods in the area of biomedical signal processing do integrate the more advanced processing algorithms with the necessary knowledge of the systems under study. Hence, to obtain innovative results in this research, it is necessary to achieve multidisciplinary and interdisciplinary knowledge; certainly, the biomedical engineer can be one of the main actors toward these ends.