Biomedical Signal Processing and Artificial Intelligence in Healthcare (Developments in Biomedical Engineering and Bioelectronics)

257 Pages·2020·15.886 MB·English

by  Walid A. Zgallai (editor)

Biomedical Signal
Processing and
Artificial Intelligence
in Healthcare
Developments in Biomedical Engineering
and Bioelectronics Series
Biomedical Signal
Processing and
Artificial Intelligence
in Healthcare
Edited by
Dr. Walid Zgallai
Series Editor
Dr. Dennis Fitzpatrick
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My contribution is dedicated to my parents, Mr. AbdulhamidZigalaie
and Mrs. Amina Ibrahim.
Dr. Walid Zgalllai
Contributors
Dr. NouraAlHinai
Higher Colleges ofTechnology, Dubai, United Arab Emirates
Dr. J. Teye Brown
FacultyofEngineeringTechnologyandScience,HigherCollegesofTechnology,
Dubai, United Arab Emirates
Christoph Janott
Munich School ofBioEngineering, TechnicalUniversity of Munich,Garching,
Germany
MusaSaniMusa
Department ofBiomedical Engineering, FacultyofEngineering, Near East
University, Nicosia,NorthernCyprus, Turkey
MubarakTaiwo Mustapha
DepartmentofBiomedicalEngineering,FacultyofEngineering;DESAMInstitute,
Near East University, Nicosia,Northern Cyprus, Turkey
Dr. Ilker Ozsahin
DepartmentofBiomedicalEngineering,FacultyofEngineering;DESAMInstitute,
Near East University, Nicosia,Northern Cyprus, Turkey
Dr. DilberUzun Ozsahin
DepartmentofBiomedicalEngineering,FacultyofEngineering;DESAMInstitute,
Near East University, Nicosia,Northern Cyprus, Turkey
Dr. Bashar Rajoub
FacultyofEngineeringTechnologyandScience,HigherCollegesofTechnology,
Dubai, United Arab Emirates
Dr. BernaUzun
DESAM Institute; Department ofMathematics, Near East University, Nicosia,
Northern Cyprus, Turkey
Dr. WalidZgallai
FacultyofEngineeringTechnologyandScience,HigherCollegesofTechnology,
Dubai, United Arab Emirates
xiii
Foreword
This series on biomedical engineering provides an up-to-date and comprehensive
reviewofthelatestinnovationswithinthefieldofbiomedicalengineeringandbioe-
lectronics. Each book inthe series collectively brings togetherarticlesand supple-
mentary learning materials to enhance or expand the reader’s knowledge of that
subject.Eachsubjectiscomprehensivelyintroducedwithin-deptharticlesbylead-
ing experts covering the latest technical innovations and biomedical engineering
solutions.
Thebookseriesisaimedatanybodywithaninterestinbiomedicalengineering
and bioelectronics whether it is on an academic or a professional level. Students
studying undergraduate or postgraduate courses will find the subject introductions
invaluabletoenhancetheiracademicstudiesandresearchprojects.Professionalbio-
medicalandotherengineersalikewillfindthattheyhaveaccesstothelatestup-to-
datearticlesandin-depthreviewsintheirsubjectfield.Thebookswillbesupported
byonlinemediamaterialtoincludeonlinepresentations,tutorials,applicationnotes,
algorithms, andworking code examples.
Biomedicalengineeringencompassesengineeringhardwareandsoftwareforthe
diagnosisandremedyofmedicalconditionsanddiseasesassociatedwiththehuman
body.Themeasurementandanalysisofphysiologicalsignalsinherentfromthebio-
logicalsystemswithinthehumanbodycanhelpidentifythecauseandeffectofmed-
icalconditions.
Thefirstbookintheseries,BiomedicalSignalProcessingandArtificialIntelli-
genceinHealthcare,providesanintroductiontothefoundationclassesofbiomedical
signals based on the physiological recordings and measurements from the human
body.Thebookthendevelopsthedifferenttechniquesusedtocaptureandanalyze
thesephysiologicalsignalsusingthelatestartificialintelligencetechniquessuchas
machine learning algorithmsthat analyze the hugeamountof data frombiological
systemsandhumantraitstoprovideanunderstandingofthephysiologicalparame-
ters.Thecomplexmodelsthataredevelopedultimatelyleadtothedevelopmentof
therapeutictechniquesandmedicaldevicesforthetreatmentandtherapyofmedical
conditions.
Thesecondbookintheseries,DrugDeliveryDevicesandTherapeuticSystems,
introducesthetechnologybehindtheincreasingnumberofbiomedicaldevicesthat
areusedtodeliverdrugtherapyinthetreatmentofvariousmedicalconditionsand
diseases.Thebookintroducesthetheorybehindmicrofluidicsandnanofluidicsand
the MEMS technology used to fabricate micropumps, reservoirs, and actuators in
implantable drug delivery systems. The release of therapeutic drugs can be con-
trolled using a variety of external physical stimuli, the medication being adminis-
tered to specific localized tissue sites or direct into the bloodstream, the spinal
canal,orthesubarachnoidspace.Usingnanotechnology,nanoparticlescanbeused
to carry anticancer drugs deep into tumors. The book also introduces drug-eluting
stents;biodegradablehydrogels;andintranasalandtransdermaldrugdelivery,with
xv
xvi Foreword
specificchaptersonmicroneedlesthatpiercetheskinusingsolidorhollowMEMS
structuresandtheconverseneedle-freeinjectors.Finallythemaintherapeuticappli-
cations of drug delivery systems are presented, including cancer, diabetes, Parkin-
son’s disease, and epilepsy.
The advancements in biomedical engineering provides not only more sophisti-
cateddiagnostictechniquesbutalsomorerealistictechnologicalsolutionstothetreat-
mentandtherapyofmedicalconditionsanddiseases.Thesebiomedicalengineering
advancements will be coveredin subsequent volumes in the book series with each
bookpresentingthelatestadvancesinbiomedicalengineeringandbioelectronics.
Dr. DennisFitzpatrick
Preface
Book chapters
The human body consists of a complex network of subsystems that perform vital
physiological processes. Biomedical signals acquired from organs using multiple
sensorscommunicateinformationaboutthephysiologicalprocessesandunderlying
pathologies. Such measurements can be plotted against time on a patient’s chart.
Analysis of these measurements provides useful information about the state of a
patient’s health.
Datamodelingandanalysistoolscanbeusedtoextractinformationfromsignals
andrevealtheunderlyingpathologythatisreflectedbytherecordedsignals.Typi-
callytheprocessstartswithextractingmeaningfulfeaturesfromthesignalandsub-
sequentlyusingthesefeaturestolearnamodelthatrelatestheextractedfeaturesto
specificconditions.However,duetothevastamountofdatarecordedandthelimited
resources,physiciansnormallyendupinactuallymakingtreatmentdecisionsbased
onobservingshortisolatedsnapshotsofbiomedicalsignals.Assuch,providingphy-
sicians with reliable automated data analysis tools offers many potential benefits
suchasprovidinguswithearlywarningsignsoftheonsetofheartattacks,improve
clinical diagnostic accuracy, and help clinicians make correct and timely medical
interventions. Chapter 1, hence, introduces the basics of biomedical signal
processing.
ThemainobjectiveofChapter2istointroducethereadertothefundamentalsof
biomedicalsignalcharacterization.Featureengineering,featureextraction,generat-
ingalternativerepresentationsofbiomedicalsignalsformthemaintheme.Wewill
first examine time-series data and how to represent them mathematically. We will
discussfeatureengineeringfromdifferentperspectives:First,webeginbystatistical
featuregenerationusingstatisticalmoments.Next,wediscusstransform-basedfea-
tureextractionusingtheFouriertransform,time-frequencyanalysis,principalcom-
ponentanalysis,independentcomponentanalysis,andfactoranalysis.Theconcepts
ofpowerspectrum,periodogram,anddimensionality reduction willbeintroduced,
andexamplesofbiomedicalsignalswillbediscussed.Wewillalsodiscussfeature
engineeringusingfeaturedescriptorandconcludewithadiscussionoffeatureselec-
tion techniques.
Chapter 3 focuses on different supervised and unsupervised machine learning
algorithmsforbiomedicalsignalanalysis.Commonalgorithmsinsupervisedlearn-
ing include classification and regression, where different algorithms will be
explainedindetails,permittingtoeffectivelyproducecorrectoutputdata.Similarly,
unsupervisedlearningalgorithmssuchasclusteringwillbediscussed.Performance
ofmachinelearningmodelswillbeillustratedusingclassificationaccuracy,region
operating characteristics, and area under the curve. Finally, applications in
xvii
xviii Preface
biomedicalsignalprocessingwillbepresentedintheformofacasestudyforauto-
mated detectionofQRS complex inECG signals based on temporal features.
Chapter 4 focuses on machine learning (ML) in biomedical signal processing
withECGapplicationsandtheadvantagesofmachinelearningtobiomedicalsignal
analysis.ThetasksperformedonECGsignalsarecommonlygeneratedbythemus-
clesoftheheartresultinginanelectricalsignal.Thisisconsideredasoneofthemost
importantphysiologicalparametersforfeatureextractionofECG,anditisthemost
essentialtaskinthemanualandautomatedECGanalysisforuseininstrumentssuch
asECGmonitors,Holtertaperecordersandscanners,andambulatoryECGrecorders
andanalyzers.Machinelearningapplicationsapplyartificialintelligenttoolssuchas
neuralnetworks,geneticalgorithms,fuzzysystems,andexpertsystemsthatarefre-
quentlyusedfordetectionanddiagnostictasks.Thischapterwillalsoaddresschal-
lengesassociated with machinelearningapplications performed on ECG signals.
Chapter5willdiscusshowtoapplydeeplearning,convolutionalneuralnetworks
to process EEG signals in applications like controlling a robotic wheelchair. The
processincludesobtainingrawelectroencephalogram(EEG)datainrealtimeutiliz-
ing a brain-computer interface (BCI) device, providing a communication medium
between brain activity signals and an external computer. These data are then pro-
cessedusingthefastFouriertransformalgorithm(FFT).Toconverttheseprocessed
dataintodataconducivetoprocessingwithconvolutionalneuralnetworks(CNNs),
Python programming language and the NumPy and scikit-learn library are used.
Finally, classification of the EEG data into four different commands is carried out
using the popular TensorFlow and Keras machine learning libraries to train a con-
volutionalneuralnetwork.Thechapterpresentsdeepconvolutionalneuralnetworks.
ThisisfollowedbyexplainingthedeploymentofTensorflowandKerasinconvolu-
tionalneuralnetworks.AstepbystepexplanationofacquiringEEGdatawithaBCI
deviceispresented.DataprocessingandtrainingutilizingTensorflowandKerasis
shown.Deploymentandrealtimeapplicationswithembeddedsystemsisintroduced.
Thechapter offers alsoa tutorial on how towork with Pandas.
Fuzzylogictechniqueoffersgoodsolutionswhenitcomestovaguenessinnat-
urallanguageandseveralotherapplicationdomains.Thisisduetoitscharacteristics
toconsidernotionsoftruthandfalsehoodinagradedfashionwhenitcomestorea-
soningsystems.Variousdiseasesvarydifferentlyinbehavioramongpatients.Also,
similarsymptomscanbecausedbydifferentdiseases,leadingtodifficultyindiag-
nosisandasaresultinmedicaltreatment.Thereforetheapplicationoffuzzylogicin
the medical field has gained popularity. For example, fuzzy expert systems were
developedandtestedinhospitalstodiagnosediseasesaffectingthelungs,syndrome
differentiation, and disease classification. Under vague conditions the physician
requiresassistancetomakeadiagnosis.Thereforefuzzylogiccanbecombinedwith
othermodelingapproachessuchasfuzzylinearprogrammingwhereitaimstodis-
tribute several treatments to different disease population to minimize human
productivity loss.
Fuzzymultiple-criteriadecisionanalysis(MCDA)isanotherimportantaspectof
operational research that has been employed in medicine because of its efficiency