discriminative sparse coding in the analysis of electrocardiogram during ventricular arrhythmias

82 Pages·2015·0.76 MB·English

DISCRIMINATIVE SPARSE CODING IN
THE ANALYSIS OF
ELECTROCARDIOGRAM
DURING VENTRICULAR
ARRHYTHMIAS
by
Iman Kalaji
B.Eng., Ryerson University, June 2012
A thesis
presented to Ryerson University
in partial fulfillment
of the requirements for the degree of
Master of Applied Science
in the Program of
Electrical and Computer Engineering
Toronto, Ontario, Canada
c Iman Kalaji 2015
(cid:13)
AUTHORSDECLARATIONFORELECTRONICSUBMISSIONOFATHESIS
I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including
anyrequiredfinalrevisions,asacceptedbymyexaminers.
IauthorizeRyersonUniversitytolendthisthesistootherinstitutionsorindividualsforthepurpose
ofscholarlyresearch.
IfurtherauthorizeRyersonUniversitytoreproducethisthesisbyphotocopyingorbyothermeans,
in total or in part, at the request of other institutions or individuals for the purpose of scholarly
research.
Iunderstandthatmythesismaybemadeelectronically availabletothepublic.
ii
Abstract
DiscriminativeSparseCodingintheAnalysisofElectrocardiogram
During VentricularArrhythmias,
MasterofAppliedScience,2015,
ImanKalaji,
Department ofElectricalandComputerEngineering,RyersonUniversity.
Abnormalities in the rhythmic electromechanical contractions of the heart results in cardiac
arrhythmias. When these abnormalities rise from the ventricles of the heart, they are classified as
ventricular arrhythmias. The two major types of ventricular arrhythmias are ventricular tachycar-
dia(VT)andventricularfibrillation(VF).Ventricularfibrillationisthemostdangerousamongthe
two arrhythmias, that usually leads to sudden cardiac death if not treated immediately. Annually
about40,000suddencardiacdeathsarereportedinCanada. Duetohighmortalityrateandserious
impactonqualityoflife,researchershavebeenfocusingoncharacterizingventriculararrhythmias
thatmayleadtodeliveringoptimizedtreatment optionsinimproving thesurvivalrates.
In this thesis two major types of ventricular arrhythmias were analyzed and quantified by
performing a discriminative sparse coding analysis called label consistent K-SVD using time-
frequency dictionaries that are well localized in time and frequency domains. The analyzed sig-
nals were 670 ECG ventricular arrhythmia segments from 33 patients extracted from the Malig-
nant Ventricular Ectopy and Creighton University Tachy-Arrhythmia databases. Using the LC-
KSVD dictionary learning approach, an overall maximum classification accuracy of 73.3% was
achieved with a hybrid optimized wavelet dictionary. Based on the comparative analysis, the
trained (learned) dictionaries yielded better performance than the untrained dictionaries. The re-
sults indicate that discriminative sparse coding approach has greater potential in extracting signal
adaptive and morphologically discriminative time-frequency structures in studying ventricular ar-
rhtyhmias.
iii
Acknowledgments
IwouldliketoexpressmysinceregratitudetomysupervisorDr. K.Umapathyforthecontinuous
support, guidance, and motivation. His guidance helped me in all the time of research, I could
not have imagined having a better adviser and mentor for my master study. I also thankfully
acknowledge Mr. Balasundaram for providing me with his help and feedback whenever I needed
it. LastbutnotleastIwouldliketothankmyparents,whoinspiredmeforhigherstudiesandhave
supportedmethroughouttheentireprocess,bothbykeepingmemotivatedandhelpingmeputting
the pieces together. I will be grateful forever for your love and support, I could not have done it
without you. The most special thank goes to my partner and friend, my husband Mohammad for
hisloveandsupportduring thedifficulttimesandforalwaysbeingsopatientwithme. Loveyou.
iv
Tomybelovedparents,
tomyhusband,
andtomybestfriendmybrother.
v
Contents
Author’sDeclaration ii
Abstract iii
Acknowledgments iv
Dedication v
ListofTables viii
ListofFigures ix
1 Introduction 1
1.1 Electrophysiology oftheCardiovascularSystem . . . . . . . . . . . . . . . . . . . 1
1.2 VentricularArrhythmias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 VentricularTachycardia . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.2 VentricularFibrillation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 ECGAnalysisDuringVentricularArrhythmias . . . . . . . . . . . . . . . . . . . 6
1.3.1 Time-DomainAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.2 Frequency-Domain Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.3 Time-FrequencyDomainAnalysis . . . . . . . . . . . . . . . . . . . . . . 8
1.4 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.5 Thesisoutline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 MethodsBackground 13
2.1 Time-FrequencyDomainAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Time-FrequencyDictionaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.1 ParametricDictionaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2.2 LearnedDictionaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.3 Label-ConsistentK-SVD . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3 LinearDiscriminantAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.4 CrossValidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.5 BackgroundSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
vi
3 LC-KSVDParametersForVentricularArrhythmiaAnalysis 32
3.1 DatabaseandPre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2 DictionarySelection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2.1 GaborDictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2.2 WaveletDictionaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2.3 Optimal Dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3 ChoiceofOptimalParameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.4 Chapter3Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4 FeatureExtractionandPatternClassification 44
4.1 Features/FeatureSpace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.2 LC-KSVDanalysisonArrhythmic Signals . . . . . . . . . . . . . . . . . . . . . . 45
4.2.1 ResultsUsingOptimizedDictionaries . . . . . . . . . . . . . . . . . . . . 46
4.2.2 ResultsUsingFullDictionary . . . . . . . . . . . . . . . . . . . . . . . . 54
4.3 ResultsSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.4 Chapter4Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5 ConclusionsandFutureWork 61
5.1 SummaryandConclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.2 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
A Abbreviations 63
B OptimizationResults 64
B.1 Sampleoptimizationresultsusingdifferentα forasubsetofdatausingGaborand
Sym4dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
B.2 Sample optimization results using different α and β for a subset of data using
GaborandSym4dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
Bibliography 66
vii
List of Tables
3.1 Classificationaccuracyandreconstructionerrorfordifferentαparametersfor2000
iterationsofLC-KSVD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.1 Averageclassificationaccuracyfordifferentcombinationsoftheoptimizeddictio-
nariesfor2000iterationsofLC-KSVD . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2 Confusion matrix of discriminating VF and VT signals using optimized Sym-
let4–Wpsym4dictionariesofLC-KSVD . . . . . . . . . . . . . . . . . . . . . . . 48
4.3 Range of the 5-folds confusion matrix of discriminating VF and VT signals using
optimizedSymlet4–Wpsym4 dictionariesofLC-KSVD . . . . . . . . . . . . . . . 49
4.4 The Classification accuracy for the five folds data sets using LC-KSVD1 and LC-
KSVD2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.5 The Classification accuracy for the five folds data sets using the optimized trained
andnon-trained Symlet4–Wpsym4dictionaries . . . . . . . . . . . . . . . . . . . 50
4.6 Average classification accuracy for different combinations of the full dictionaries
for2000iterations ofLC-KSVD . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.7 ConfusionmatrixofdiscriminatingVFandVTsignalsusingfullGabor–Wpsym4
dictionariesofLC-KSVD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.8 Range of the 5-folds confusion matrix of discriminating VF and VT signals using
fullGabor–Wpsym4dictionary ofLC-KSVD . . . . . . . . . . . . . . . . . . . . 58
4.9 TheClassificationaccuracyforthefourdatasetsusingnon-trainedandLC-KSVD
trainedfullGabor–Wpsymlet4 dictionary . . . . . . . . . . . . . . . . . . . . . . 58
B.1 Classification accuracy and error 1 for different α parameters for 2000 iterations
ofLC-KSVD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
B.2 Total error 2 for different combinations of α and β parameters for 2000 iterations
ofLC-KSVD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
B.3 Classification accuracy for different combinations of α and β parameters for 2000
iterationsofLC-KSVD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
viii
List of Figures
1.1 Theanatomyoftheheart c McGraw-Hill[1] . . . . . . . . . . . . . . . . . . . . 2
(cid:13)
1.2 Theelectricalconductionpathwayoftheheart c McGraw-Hill[1] . . . . . . . . . 3
(cid:13)
1.3 Theelectricalconductionofnormalsinusrhythm c McGraw-Hill [1] . . . . . . . 4
(cid:13)
1.4 Three second ventricular tachycardia (top), three second ventricular fibrillation
(bottom) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.5 Thesisoutline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1 a) Coverage of the time-frequency plane for the STFT. b) Coverage of the time-
frequency plane for the wavelet. c) Corresponding basis function of the STFT. d)
Correspondingbasisfunction ofthewavelets[2] . . . . . . . . . . . . . . . . . . 14
2.2 GaborAtom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3 Mfoldcrossvalidationstructure . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.1 SamplesignalsofVFandVT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2 Four different Gabor atoms from the generated dictionary of different translation
andscales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3 FourdifferentWaveletatomsfrom thegenerateddictionaries . . . . . . . . . . . . 36
3.4 Matching pursuit on VF signal (Top) and it’s captured energy (bottom) using Dic-
tionary1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.5 Matchingpursuit onVT signal (Top)andit’s captured energy(bottom) usingDic-
tionary2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.6 Matching pursuit on VF signal (Top) and it’s captured energy (bottom) using Dic-
tionary3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.7 Classificationaccuraciesusingdifferent α values . . . . . . . . . . . . . . . . . . 41
3.8 Totalerror1usingdifferentα values . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.9 Boxplotoftheclassificationaccuracyrangesusingdifferentα andβ values . . . . 42
3.10 Boxplotofthetotalerror2rangesusingdifferentα andβ values . . . . . . . . . . 43
4.1 An example of feature space of the estimated score feature, where blue pluses
representsVFtestedsignalsandredcirclesrepresentsVTtestedsignals . . . . . . 45
4.2 Flowchartoftheresultsanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.3 BoxplotofclassificationaccuraciesofLC-KSVD1andLC-KSVD2 . . . . . . . . 49
ix
4.4 BoxplotoftheclassificationaccuracyofthefivefolddatasetsofVFandVTusing
optimizedtrainedandnon-trained Symlet4–Wpsymlet4 dictionaries . . . . . . . . 51
4.5 ExamplesofSymlet4–Wpsymlet4 traineddictionary atoms . . . . . . . . . . . . . 51
4.6 BoxplotoftheclassificationaccuracyofthefivefolddatasetsofVFandVTusing
optimizedtrainedandnon-trained Gabor–Wpsymlet4 dictionaries . . . . . . . . . 52
4.7 BoxplotoftheclassificationaccuracyofthefivefolddatasetsofVFandVTusing
optimizedtrainedandnon-trained Gabor–Symlet4dictionaries . . . . . . . . . . . 53
4.8 ClassificationaccuracyusingdifferentsizesoftheGabor–Wpsym4dictionary . . . 54
4.9 Computational time of one fold using LC-KSVD with respect to different sizes of
theGabor–Wpsym4dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.10 ComparingdifferenttypesofclassifiersforclassifyingthefeaturescoreoftheLC-
KSVD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.11 Boxplot of the classification accuracies of the five folds using trained and non-
trainedGabor–Wpsymlet4dictionary . . . . . . . . . . . . . . . . . . . . . . . . . 58
x