Advanced Biosignal Processing PDF
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Advanced Biosignal Processing A. Na¨ıt-Ali (Ed.) Advanced Biosignal Processing 123 Assoc.Prof.AmineNa¨ıt-Ali Universite´ Paris12 Labo.Images,Signauxet Syste`mes Intelligents (LISSI) EA 3956 61av.duGe´ne´raldeGaille 94010Cre´teil France [email protected] ISBN 978-3-540-89505-3 e-ISBN 978-3-540-89506-0 DOI 10.1007/978-3-540-89506-0 LibraryofCongressControlNumber:2008910199 (cid:2)c Springer-VerlagBerlinHeidelberg2009 Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerialis concerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,broadcasting, reproductiononmicrofilmorinanyotherway,andstorageindatabanks.Duplicationofthispublication orpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember9, 1965,initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Violationsare liabletoprosecutionundertheGermanCopyrightLaw. Theuseofgeneraldescriptivenames,registerednames,trademarks,etc.inthispublicationdoesnotimply, evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevantprotectivelaws andregulationsandthereforefreeforgeneraluse. Coverdesign:WMXDesignGmbH,Heidelberg Printedonacid-freepaper 9 8 7 6 5 4 3 2 1 springer.com This Page Intentionally Left Blank Preface Generally speaking, Biosignals refer to signals recorded from the human body. Theycanbeeitherelectrical(e.g.Electrocardiogram(ECG),Electroencephalogram (EEG),Electromyogram(EMG),etc.)ornon-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 cliniciansandphysicianswithusefulinformationduringdiagnosticandmonitoring processes.Insomeapplications,thepurposeisnotnecessarilymedical.Itmayalso beindustrial.Forinstance,areal-timeEEGsystemanalysiscanbeusedtocontrol andanalyzethevigilanceofacardriver.Inthiscase,thepurposeofsuchasystem basically consists of preventing crash risks. Furthermore, in certain other applica- tions,asetofbiosignals(e.g.ECG,respiratorysignal,EEG,etc.)canbeusedtocon- trolor analyze human emotions. This isthe case of the famous polygraph system, alsoknownasthe“liedetector”,theefficiencyofwhichremainsopentodebate! Thus when one is dealing with biosignals, special attention must be given to theiracquisition,theiranalysisandtheirprocessingcapabilitieswhichconstitutethe finalstageprecedingtheclinicaldiagnosis.Naturally,thediagnosisisbasedonthe informationprovidedbytheprocessingsystem.Insuchcases,hugeresponsibilityis placedonthissystemandinsomecountries,legislationrelatingtoclinicalpractices isparticularlyimportant! Therefore, specific attention should be paid to the way these signals have to be processed. As you are aware, clinicians dealing with processed signals care little aboutthealgorithmthathasbeenimplementedintheirsystemtoextracttherequired information.Forthem,thefinalresultsareallthatcounts!Wesharethisopinion! Anotherremark:itshouldbenotedthatcomplexandsophisticatedalgorithmsdo not systematically lead to better results! It is clear that this doesn’t mean that one should use simple algorithms instead. In fact, in such cases everything is relative! Hence, the following question arises: what is meant exactly by the phrase ‘good results’?Whatdoesasophisticatedalgorithmmean?Wecanguessthatmorethan onedefinitioncanbeemployed.Agivenresultisevaluatedintermsofthepurposeof theapplicationandthecriterionusedfortheevaluation.Generallyspeaking,signal processing and, in particular digital signal processing, has today become a huge universe,incorporatingawiderangeoftechniquesandapproaches.Makingoneself aware of the whole corpus of published techniques is far from straightforward. In v vi Preface fact,manyfieldsdealwithdigitalsignalprocessingincludingmathematics,electri- calengineering,computerengineeringandsoon.Consequently,thesameproblem willneverbesolvedfromthesamepointofview.Inotherwords,insomescientist communities,onemaydevelopasignalprocessingtoolbeforeverifyingwhetherit canfitagivenapplication.Conversely,otherscientistsstudyandanalyzeaproblem then tryto develop a technique that fits the concerned application. So which strat- egyshouldoneuse?Iamquitesurethatthereaderwillfavouronestrategyabove another depending on his background. There is potential for huge debate on this subject!Frommypointofview,bothhavetheirmertisandaresometimescomple- mentary. Combining approaches, ideas and experiences, especially when working onaspecificproblemmayleadtointerestingresults. In this book, the purpose is not to offer the reader an account of all the tech- niqueswhichcanbeusedtoprocessanygivenbiosignal.Thiswouldbeveryambi- tious and unrealistic. The reason for this being that a given biosignal can be pro- cessed differently depending on the target defined by the application. Moreover, wewillnotbefocusingonclassicaltechniquesthatmightalsobeefficient.Hence, the reader shouldn’t confuse simplicity with the efficiency. As emphasized by the book’stitle,“AdvancedBiosignalProcessing”,whichcouldalso,implicitlyberead as: “Advanced Digital Signal Processing Techniques applied on Biosignals”; the purposeistoprovide,insomeway,anin-depthconsiderationoftheparticularori- entationsregardingthewayonecanprocessaspecificbiosignalusingvariousrecent signalprocessingtools. Manyscientistshavecontributedtothisbook.Theyrepresentseverallaboratories based around the world. As a result, the reader can gain access to a wide panel of strategiesfordealingwithcertainbiosignals.Isay“certainbiosignals”,becauseour choiceis,infact,restrictedmainlyto“bio-electricalsignals”andespeciallytothose most frequently used in clinical routines such as ECG, EEG, EPs and EMG. For other types of biosignals, one may refer directly to other works published specifi- callyonthese. The idea of this book is to explore the maximum level of advanced signal pro- cessing techniques rather than scanning the maximum number of biosignals. The intentionistoassistthereaderinmakingdecisionsregardinghisownapproachin his project of interest; perhaps by mixing two or more techniques, or improving sometechniquesand,whyevernot,proposingentirelynewones?Asyouareaware, thereisnosuchthingasperfectioninresearch.Sothereadercanalwaysthinkabout makingsomething“evenbetter”.Forinstance,thismighttaketheform:“howcanI getthesameresultbutfaster?”(thinkaboutthecomplexityofyouralgorithms);or “howcanIgetbetterresultswithoutincreasingthecomplexity”andsoon. On the other hand, researchers might face problems when evaluating a new approach.Thisproblembasicallyconcernsthedatabaseuponwhichonehastoeval- uatehisalgorithms.Insomecases,theevaluationisachievedonaconfidentiallocal databaseprovidedbyagivenmedicalinstitution.Theconstraintinsuchsituations isthatthesedatacannotbeshared. In some other cases, some international databases available on the Internet can beusedforthispurpose(e.g.Physionet).Amongthesedatabases,specialattention Preface vii has been given to MeDEISA “Medical Database for the Evaluation of Image and Signal Processing Algorithms”, at www.medeisa.net. The specificity of MeDEISA whichhasbeenassociatedwiththisbookisthatdatacanbepostedfromscientists owningsomeparticulardata,recordedundercertainspecificconditions.Thesedata are downloadable in MATLAB format and can be subject to various processing. Therefore,sinceeachsignalisidentifiablebyitsreference,webelievethatthiswill be, in the future a good way to evaluate and compare objectively any published signalprocessingtechnique. This book is intended for final year undergraduate students, postgraduate students, engineers and researchers in biomedical engineering and applied digital signalprocessing.Ithasbeendividedintofourspecificsections.Eachsectioncon- cernsoneofthebiosignalspointedoutabove,namelytheECG,EEG,EMGandthe EPs. The “Epilogue” deals with some general purpose techniques and multimodal processing.Consequently,numerousadvancedsignalprocessingmethodsarestud- iedandanalyzedsuchas: (cid:2) (cid:2) Sourceseparation, (cid:2) Statisticalmodels, (cid:2) Metaheuristics, (cid:2) Timefrequency, (cid:2) Adaptivetracking, (cid:2) Waveletsneuralnetworksandwaveletnetworks, (cid:2) Modelinganddetection, (cid:2) WaveletandChirplettransforms, (cid:2) Non-linearandEMDapproaches, Compression. Of course, to deal with these subjects, we assume that the reader is familiar with basicdigitalsignalprocessingmethods. Thesetechniquesarepresentedthrough17chapters,structuredasfollows: (cid:2) Chapter1“Biosignals:propertiesandacquisition”:Thiscanberegardedasan introductory chapter in which some generic acquisition schemes are presented. For obvious reasons, some well known general biosignal properties are also (cid:2) evoked. Chapter 2 “Extraction of ECG characteristics using source separation tech- niques:exploitingstatisticalindependenceandbeyond”:Thischapterdealswith theBlindSource Separation (BSS)approach. Special attentionisgiven tofetal (cid:2) ECGextraction. Chapter3“ECGprocessingforexercisetest”:Inthischapterconceptsofmod- elingandestimationtechniquesarepresentedforthepurposeofextractingfunc- (cid:2) tionalclinicalinformationfromECGrecordings,duringanexercisetest. Chapter4“StatisticalmodelsbasedECGclassification”:Theauthorsdescribe, over the course of this chapter, how one can use hidden Markov models and hiddenMarkovtreesforthepurposeofECGbeatmodelingandclassification. viii Preface (cid:2) Chapter5“HeartRateVariabilitytime-frequencyanalysisfornewbornseizure detection”: In this chapter, time-frequency analysis techniques are discussed and applied to the ECG signal for the purpose of automatic seizure detection. The authors explain how the presented technique can be combined with the (cid:2) EEG-basedmethodologies. Chapter 6 “Adaptive tracking of EEG frequency components”: The authors addresstheproblemoftrackingoscillatorycomponentsinEEGsignals.Forthis purpose, they explain how one can use an adaptive filter bank as an efficient (cid:2) signalprocessingtool. Chapter7“FromEEGsignalstobrainconnectivity:methodsandapplications in epilepsy”: In this chapter, 3 different approaches, namely, linear and nonlin- ear regression, phase synchronization, and generalized synchronization will be (cid:2) reviewedforthepurposeofEEGanalysis. Chapter 8 “Neural Network approaches for EEG classification”: This chap- ter provides a state-of-the-art review of the prominent neural network based (cid:2) approachesthatcanbeemployedforEEGclassification. Chapter9“Analysisofevent-relatedpotentialsusingwaveletnetworks”:Inthis chapterwaveletnetworksareemployedtodescribeautomaticallyERPsusinga (cid:2) smallnumberofparameters. Chapter10“Detectionofevokedpotentials”:Thischapterisbasedondecision theory.Appliedtovisualevokedpotentials,itwillbeshownhowthestimulation (cid:2) andthedetectioncanbecombinedsuitably. Chapter 11 “Visual Evoked Potential Analysis Using Adaptive Chirplet Trans- form”.Afterexplainingthetransitionfromthewavelettothechirplet,thisnew (cid:2) transformisappliedandevaluatedonVEPs. Chapter 12 “Uterine EMG analysis: time-frequency based techniques for pretermbirthdetection”.Inthischapter,globalsignalprocessingwavelet-based and neural network-based systems will be described for the purpose of: detec- (cid:2) tion,classification,identificationanddiagnosticoftheuterineEMG. Chapter13“PatternclassificationtechniquesforEMGsignaldecomposition”. The electromyographic (EMG) signal decomposition process is addressed by developingdifferentapproachestopatternclassification.Forthispurpose,single (cid:2) classifierandmulticlassifierapproachesarepresented. Chapter 14 “Parametric modeling of biosignals using metaheuristics”. Two main metaheuristic techniques will be presented, namely Genetic Algorithms and the Swarm Particle Optimization algorithm. They will be used to model somebiosignals,namelyBrainstemAuditoryEvokedPotentials,EventRelated (cid:2) PotentialsandECGbeats. Chapter 15 “Nonlinear analysis of physiological time series”: The aim of this chapteristoprovideareviewofthemainapproachestononlinearanalysis(frac- talanalysis,chaostheory,complexitymeasures)inphysiologicalresearch,from (cid:2) systemmodelingtomethodologicalanalysisandclinicalapplications. Chapter 16 “Biomedical data processing using HHT: a review”: In this chap- ter,biomedicaldataprocessingisreviewedusingHilbert-HuangTransform,also calledEmpiricalModeDecomposition(EMD). Preface ix (cid:2) Chapter17“Introductiontomultimodalcompressionofbiomedicaldata”:The aimofthischapteristoprovidethereaderwithanewvisionofcompressingboth medical “images/videos” and “biosignals” jointly. This type of compression is called“multimodalcompression”. Through these chapters, I hope that the reader will find this book useful and con- structive and that the evoked approaches will contribute efficiently, by providing innovative ideas to be applied in this, so fascinating a field, by which I mean, of course,BiosignalProcessing! Finally, I would like to thank all the authors for their active and efficient contribution. Cre´teil,France A.Na¨ıt-Ali
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