Signal processing and machine learning for biomedical big data

624 Pages·2018·72.692 MB·English

by  Falk, Tiago H., Sejdic, Ervin

Signal Processing and Machine
Learning for Biomedical Big Data
http://taylorandfrancis.com
Signal Processing and Machine
Learning for Biomedical Big Data
Edited by
ć
Ervin Sejdi
Tiago H. Falk
MATLAB®isatrademarkofTheMathWorks,Inc.andisusedwithpermission.TheMathWorksdoesnotwarranttheaccuracyofthetextorexercisesinthisbook.
Thisbook’suseordiscussionofMATLAB®softwareorrelatedproductsdoesnotconstituteendorsementorsponsorshipbyTheMathWorksofaparticular
pedagogicalapproachorparticularuseoftheMATLAB®software.
CRCPress
Taylor&FrancisGroup
6000BrokenSoundParkwayNW,Suite300
BocaRaton,FL33487-2742
©2018byTaylor&FrancisGroup,LLC
CRCPressisanimprintofTaylor&FrancisGroup,anInformabusiness
NoclaimtooriginalU.S.Governmentworks
Printedonacid-freepaper
InternationalStandardBookNumber-13:978-1-4987-7345-4(Hardback)
Thisbookcontainsinformationobtainedfromauthenticandhighlyregardedsources.Reasonableeffortshavebeenmadetopublishreliabledataandinformation,
buttheauthorandpublishercannotassumeresponsibilityforthevalidityofallmaterialsortheconsequencesoftheiruse.Theauthorsandpublishershave
attemptedtotracethecopyrightholdersofallmaterialreproducedinthispublicationandapologizetocopyrightholdersifpermissiontopublishinthisformhasnot
beenobtained.Ifanycopyrightmaterialhasnotbeenacknowledgedpleasewriteandletusknowsowemayrectifyinanyfuturereprint.
ExceptaspermittedunderU.S.CopyrightLaw,nopartofthisbookmaybereprinted,reproduced,transmitted,orutilizedinanyformbyanyelectronic,mechanical,
orothermeans,nowknownorhereafterinvented,includingphotocopying,microfilming,andrecording,orinanyinformationstorageorretrievalsystem,without
writtenpermissionfromthepublishers.
Forpermissiontophotocopyorusematerialelectronicallyfromthiswork,pleaseaccesswww.copyright.com(http://www.copyright.com/)orcontacttheCopyright
ClearanceCenter,Inc.(CCC),222RosewoodDrive,Danvers,MA01923,978-750-8400.CCCisanot-for-profitorganizationthatprovideslicensesandregistration
foravarietyofusers.FororganizationsthathavebeengrantedaphotocopylicensebytheCCC,aseparatesystemofpaymenthasbeenarranged.
TrademarkNotice:Productorcorporatenamesmaybetrademarksorregisteredtrademarks,andareusedonlyforidentificationandexplanationwithoutintentto
infringe.
LibraryofCongressCataloging-in-PublicationData
Names:Sejdic,Ervin,author.|Falk,TiagoH.,author.
Title:Signalprocessingandmachinelearningforbiomedicalbigdata/ErvinSejdicandTiagoH.Falk.
Description:BocaRaton:Taylor&Francis,2018.|Includesbibliographicalreferences.
Identifiers:LCCN2017053075|ISBN9781498773454(hardback:alk.paper)|ISBN9781498773461(ebook)
Subjects:|MESH:MedicalInformatics|DataCollection|SignalProcessing,Computer-Assisted|
MachineLearning
Classification:LCCR855.3|NLMW26.5|DDC610.285--dc23
LCrecordavailableathttps://lccn.loc.gov/2017053075
VisittheTaylor&FrancisWebsiteat
http://www.taylorandfrancis.com
andtheCRCPressWebsiteat
http://www.crcpress.com
To my family! Foryour unconditional love andsupport.
Ervin Sejdić
ToEric and Samuel. Foryour unconditional love andendless supplyof giggles and laughter.I love you, forever!
TiagoH. Falk
http://taylorandfrancis.com
Contents
Preface.........................................................................................................................................................................................ix
Editors.........................................................................................................................................................................................xi
Contributors.............................................................................................................................................................................xiii
Section I Introduction
1. Signal Processing inthe Era ofBiomedical Big Data.................................................................................................3
Tiago H. Falkand Ervin Sejdić
2. Collecting andMaking Sense ofBig Data forImproved Health Care...................................................................9
Thomas R. Clancy
3. Big DataEra inMagnetic Resonance Imaging ofthe HumanBrain.....................................................................21
Xiaoyu Ding, Elisabeth deCastro Caparelli, and Thomas J. Ross
Section II Signal Processing for Big Data
4. Data-Driven Approachesfor Detecting andIdentifying Anomalous DataStreams..........................................57
Shaofeng Zou, Yingbin Liang, H. Vincent Poor, andXinghua Shi
5. Time–Frequency Analysis for EEG Quality Measurementand Enhancementwith Applications
inNewborn EEG Abnormality Detection Multichannel EEG Enhancement andClassification
forNewborn Health Outcome Prediction...................................................................................................................73
Boualem Boashash, Samir Ouelha, Mohammad Al-Sa’d,Ayat Salim, and Paul Colditz
6. Active Recursive Bayesian State Estimation forBig Biological Data..................................................................115
Mohammad Moghadamfalahi, Murat Akcakaya, andDeniz Erdogmus
7. Compressive Sensing Methods for Reconstruction of BigSparse Signals........................................................133
Ljubiša Stanković,Miloš Daković,and Isidora Stanković
8. Low-Complexity DCTApproximations forBiomedical Signal Processing in Big Data.................................151
Renato J. Cintra, Fábio M. Bayer, YvesPauchard, andArjuna Madanayake
9. Dynamic Processeson Complex Networks...............................................................................................................177
June Zhangand José M.F. Moura
10. Modeling Functional Networks via Piecewise-Stationary Graphical Models...................................................193
Hang Yu andJustin Dauwels
11. Topological DataAnalysis ofBiomedical Big Data................................................................................................209
Angkoon Phinyomark, EstherIbañez-Marcelo, andGiovanni Petri
12. Targeted Learning with Application toHealth Care Research.............................................................................235
Susan Gruber
vii
viii Contents
Section III Applications of Signal Processing and Machine Learning
for Big Biomedical Data
13. ScalableSignalDataProcessingforMeasuringFunctionalConnectivityinEpilepsyNeurologicalDisorder....259
Arthur Gershon, Samden D. Lhatoo, Curtis Tatsuoka, Kaushik Ghosh, Kenneth Loparo, and SatyaS. Sahoo
14. Machine Learning Approaches toAutomatic Interpretation ofEEGs................................................................271
IyadObeid and Joseph Picone
15. Information Fusion inDeep Convolutional Neural Networks forBiomedical Image Segmentation..........301
Mohammad Havaei, Nicolas Guizard, Nicolas Chapados,and Yoshua Bengio
16. Automated Biventricular Cardiovascular Modelling fromMRI for Big Heart DataAnalysis......................313
Kathleen Gilbert, Xingyu Zhang,Beau Pontré, Avan Suinesiaputra, Pau Medrano-Gracia, and Alistair Young
17. Deep Learning for Retinal Analysis...........................................................................................................................329
HenryA. Leopold, John S.Zelek, andVasudevan Lakshminarayanan
18. Dictionary Learning Applications for HEp-2 Cell Classification.........................................................................369
Sadaf Monajemi, Shahab Ensafi, Shijian Lu, Ashraf A. Kassim, Chew Lim Tan, SaeidSanei, and Sim-Heng Ong
19. Computational Sequence-and NGS-Based MicroRNA Prediction.....................................................................381
R.J. Peace and James R.Green
20. Bayesian Classification ofGenomic Big Data..........................................................................................................411
Ulisses M. Braga-Neto, EmreArslan, Upamanyu Banerjee, andArghavan Bahadorinejad
21. Neuroelectrophysiology ofSleep andInsomnia.....................................................................................................429
Ramiro Chaparro-Vargas, Beena Ahmed, Thomas Penzel, and DeanCvetkovic
22. Automated Processing of BigData inSleep Medicine..........................................................................................443
Sara Mariani, Shaun M.Purcell, and Susan Redline
23. Integrating Clinical Physiological Knowledge at theFeature andClassifier Levelsin Design
ofaClinical Decision Support System forImproved Prediction ofIntensive Care Unit Outcome.............465
Ali Jalali, Vinay M.Nadkarni, Robert A. Berg,Mohamed Rehman, andC. Nataraj
24. Trauma Outcome Prediction in theEra ofBig Data: From DataCollection to Analytics...............................477
Shiming Yang,Peter F. Hu,and ColinF. Mackenzie
25. Enchancing Medical Problem Solving through the Integration ofTemporal Abstractions
with Bayesian Networks in Time-Oriented Clinical Domains.............................................................................493
Kalia Orphanou, Athena Stassopoulou, and ElpidaKeravnou
26. Big Datain Critical Care Using Artemis...................................................................................................................519
Carolyn McGregor
27. Improving Neurorehabilitation ofthe Upper Limb through BigData...............................................................533
José Zariffa
28. Multimodal Ambulatory Fall Risk Assessment inthe Eraof BigData..............................................................551
Mina Nouredanesh and James Tung
Index.........................................................................................................................................................................................581
Preface
Bigdatahasbeenlooselydefinedasdatathatistoolarge as data quality, data compression, and new statistical
orcomplexfortraditionaldataprocessingtechniquesto andgraphsignalprocessingtechniques.Italsoprovidesa
be applied effectively. The challenges that arise with comprehensive overview of existing state-of-the-art sig-
suchmassivedatasetsincludecapture,storage,analysis, nalprocessingandmachinelearningtechniquesapplied
search, and visualization, to name a few. Within the to big biomedical data within neuroimaging, cardiac,
healthcaredomain,thedefinitionofbiomedicalbigdata retinal,genomic,sleep,patientoutcomeprediction, crit-
has been expanded to encompass “high volume, high icalcare,andrehabilitationdomains.
diversitybiological,clinical,environmental,andlifestyle Thebookwasconceptualizedwiththegoalofbringing
information collected from single individuals to large together more theoretical signal processing chapters
cohorts, in relation to their health and wellness status, describing tools aimed at big data (be it biomedical or
at one or several time points.”1 As pointed out by the not) with more application-driven chapters focusing on
Ponemon Institute, in 2012, 30% of all electronic data existing applications of signal processing and machine
stored in the world was occupied by the healthcare learning for big biomedical data. As such, the book is
industry. It is clear that buried deep within these vast aimedatbothresearchersalreadyworkinginthefieldas
amounts of data lies invaluable hidden knowledge that well as undergraduate and graduate students eager to
not only could change a patient’s life, but could open learn how signal processing can help with big data
doorstonewtherapies,drugs,diagnostictools,andgene analysis. It is hoped that the book will bring together
discoveries,thusultimatelyimprovingourpopulation’s signal processing and machine learning researchers to
qualityof life. unlock existing bottlenecks within the healthcare field
While important steps have already been taken to and, ultimately, improve patient quality of life. We
leveragesuchinsightsfrom,e.g.,neuroimagingbigdata would like to thank all contributing authors for their
or fitness data collected by wearables giants such as excellent chapters.
Fitbit, we have only scratched the surface of this data
iceberg. As more data has become available over the TiagoH. Falk
years, it has enabled important advances in machine ErvinSejdić
learning, specifically in deep learning and deep neural
network architectures, thus redefining the performance
envelope of existing technologies, such as object recog-
nition from images and automatic speech recognition.
Black-boxmachinelearningapproaches,however,while Reference
very useful indeed, are only as good as the data they
1. Auffray C, Balling R, Barroso I, Bencze L, Benson M,
aretrainedon.Ultimately,tomakesignificantprogress, Bergeron J et al. Making sense of big data in health
expert domain knowledge and signal processing tech- research: Towards an EU action plan. Genome Med.
niques are still needed. This is particularly true for bio- 2016;8(1):71.
medical data.
Bigbiomedicaldataisoftencapturedviaamultitudeof
sensorsandmodalities,eachofvaryingsamplerateand
dimensionality. Patient-generated data is often of vary-
ingquality.Themajorityofexistingdataisunstructured MATLAB®isaregisteredtrademarkofTheMathWorks,
and unlabeled. Video resolutions are doubling almost Inc. For product information, please contact:
every few years, with resolutions now at 8K ultra-high
definition.Capturing,analyzing,storing,searching,and The MathWorks, Inc.
visualizingsuchmassivedatahaverequirednewshiftsin 3Apple Hill Drive
signalprocessingparadigmsandnewwaysofcombining Natick, MA01760-2098 USA
signalprocessingwithmachinelearningtools.Thisbook Tel: [phone]
aimsatcoveringseveraloftheseaspects.Whileitdoesnot Fax: [phone]
touch upon important topics, such as data sharing and Email: [e-mail]
privacy, it does touch upon several other factors, such Web: www.mathworks.com
ix