Advanced Biosignal Processing

Through 17 chapters, this book presents the principle of many advanced biosignal processing techniques. After an important chapter introducing the main biosignal properties as well as the most recent acquisition techniques, it highlights five specific parts which build the body of this book. Each part concerns one of the most intensively used biosignals in the clinical routine, namely the Electrocardiogram (ECG), the Elektroenzephalogram (EEG), the Electromyogram (EMG) and the Evoked Potential (EP). In addition, each part gathers a certain number of chapters related to analysis, detection, classification, source separation and feature extraction. These aspects are explored by means of various advanced signal processing approaches, namely wavelets, Empirical Modal Decomposition, Neural networks, Markov models, Metaheuristics as well as hybrid approaches including wavelet networks, and neuro-fuzzy networks. The last part, concerns the Multimodal Biosignal processing, in which we present two different chapters related to the biomedical compression and the data fusion. Instead organising the chapters by approaches, the present book has been voluntarily structured according to signal categories (ECG, EEG, EMG, EP). This helps the reader, interested in a specific field, to assimilate easily the techniques dedicated to a given class of biosignals. Furthermore, most of signals used for illustration purpose in this book can be downloaded from the Medical Database for the Evaluation of Image and Signal Processing Algorithm. These materials assist considerably the user in evaluating the performances of their developed algorithms. This book is suited for final year graduate students, engineers and researchers in biomedical engineering and practicing engineers in biomedical science and medical physics.

through 17 Chapters, This Book Presents The Principle Of Many Advanced Biosignal Processing Techniques. After An Important Chapter Introducing The Main Biosignal Properties As Well As The Most Recent Acquisition Techniques, It Highlights Five Specific Parts Which Build The Body Of This Book. Each Part Concerns One Of The Most Intensively Used Biosignals In The Clinical Routine, Namely The Electrocardiogram (ecg), The Elektroenzephalogram (eeg), The Electromyogram (emg) And The Evoked Potential (ep). In Addition, Each Part Gathers A Certain Number Of Chapters Related To Analysis, Detection, Classification, Source Separation And Feature Extraction. These Aspects Are Explored By Means Of Various Advanced Signal Processing Approaches, Namely Wavelets, Empirical Modal Decomposition, Neural Networks, Markov Models, Metaheuristics As Well As Hybrid Approaches Including Wavelet Networks, And Neuro-fuzzy Networks.

the Last Part, Concerns The Multimodal Biosignal Processing, In Which We Present Two Different Chapters Related To The Biomedical Compression And The Data Fusion.

instead Organising The Chapters By Approaches, The Present Book Has Been Voluntarily Structured According To Signal Categories (ecg, Eeg, Emg, Ep). This Helps The Reader, Interested In A Specific Field, To Assimilate Easily The Techniques Dedicated To A Given Class Of Biosignals. Furthermore, Most Of Signals Used For Illustration Purpose In This Book Can Be Downloaded From The Medical Database For The Evaluation Of Image And Signal Processing Algorithm. These Materials Assist Considerably The User In Evaluating The Performances Of Their Developed Algorithms.

this Book Is Suited For Final Year Graduate Students, Engineers And Researchers In Biomedical Engineering And Practicing Engineers In Biomedical Science And Medical Physics.

Generally speaking, Biosignals refer to signals recorded from the human body. They can be either electrical (e. g. Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG), etc. ) or non-electrical (e. g. breathing, movements, etc. ). The acquisition and processing of such signals play an important role in clinical routines. They are usually considered as major indicators which provide clinicians and physicians with useful information during diagnostic and monitoring processes. In some applications, the purpose is not necessarily medical. It may also be industrial. For instance, a real-time EEG system analysis can be used to control and analyze the vigilance of a car driver. In this case, the purpose of such a system basically consists of preventing crash risks. Furthermore, in certain other appli- tions,asetof biosignals (e. g. ECG,respiratorysignal,EEG,etc. ) can be used toc- trol or analyze human emotions. This is the case of the famous polygraph system, also known as the "lie detector", the ef ciency of which remains open to debate! Thus when one is dealing with biosignals, special attention must be given to their acquisition, their analysis and their processing capabilities which constitute the nal stage preceding the clinical diagnosis. Naturally, the diagnosis is based on the information provided by the processing system. Front Matter....Pages i-xvi Biosignals: Acquisition and General Properties....Pages 1-13 Extraction of ECG Characteristics Using Source Separation Techniques: Exploiting Statistical Independence and Beyond....Pages 15-47 ECG Processing for Exercise Test....Pages 49-69 Statistical Models Based ECG Classification....Pages 71-93 Heart Rate Variability Time-Frequency Analysis for Newborn Seizure Detection....Pages 95-121 Adaptive Tracking of EEG Frequency Components....Pages 123-144 From EEG Signals to Brain Connectivity: Methods and Applications in Epilepsy....Pages 145-164 Neural Network Approaches for EEG Classification....Pages 165-182 Analysis of Event-Related Potentials Using Wavelet Networks....Pages 183-199 Detection of Evoked Potentials....Pages 201-220 Visual Evoked Potential Analysis Using Adaptive Chirplet Transform....Pages 221-244 Uterine EMG Analysis: Time-Frequency Based Techniques for Preterm Birth Detection....Pages 245-266 Pattern Classification Techniques for EMG Signal Decomposition....Pages 267-289 Parametric Modeling of Some Biosignals Using Optimization Metaheuristics....Pages 291-305 Nonlinear Analysis of Physiological Time Series....Pages 307-333 Biomedical Data Processing Using HHT: A Review....Pages 335-352 Introduction to Multimodal Compression of Biomedical Data....Pages 353-374 Back Matter....Pages 375-378 Presents the principle of many advanced biosignal processing techniques. This title introduces the main biosignal properties and the acquisition techniques. It concerns one of the most intensively used biosignals in the clinical routine, namely the Electrocardiogram, the Elektroenzephalogram, the Electromyogram and the Evoked Potential