Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear

25 Pages·2017·4.06 MB·English

sensors
Article
Automated Method for Discrimination of
Arrhythmias Using Time, Frequency, and Nonlinear
Features of Electrocardiogram Signals
ShirinHajeb-Mohammadalipour1,MohsenAhmadi1,RezaShahghadami1andKiH.Chon2,*
1 DepartmentofBiomedicalEngineering,FacultyofMedicine,ShahidBeheshtiUniversityofMedicalSciences,
Tehran1985717443,Iran;[email protected](S.H.-M.);[email protected](M.A.);
[email protected](R.S.)
2 DepartmentofBiomedicalEngineering,UniversityofConnecticut,Storrs,CT06269,USA
* Correspondence:[email protected];Tel.:[phone]
(cid:1)(cid:2)(cid:3)(cid:1)(cid:4)(cid:5)(cid:6)(cid:7)(cid:8)(cid:1)
(cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:6)(cid:7)
Received:5June2018;Accepted:26June2018;Published:29June2018
Abstract:Wedevelopedanautomatedapproachtodifferentiatebetweendifferenttypesofarrhythmic
episodesinelectrocardiogram(ECG)signals,because,inreal-lifescenarios,asoftwareapplication
doesnotknowinadvancethetypeofarrhythmiaapatientexperiences. Ourapproachhasfourmain
stages: (1)Classificationofventricularfibrillation(VF)versusnon-VFsegments—includingatrial
fibrillation(AF),ventriculartachycardia(VT),normalsinusrhythm(NSR),andsinusarrhythmias,
such as bigeminy, trigeminy, quadrigeminy, couplet, triplet—using four image-based phase plot
features,onefrequencydomainfeature,andtheShannonentropyindex.(2)ClassificationofAFversus
non-AFsegments. (3)Prematureventricularcontraction(PVC)detectiononeverynon-AFsegment,
using a time domain feature, a frequency domain feature, and two features that characterize the
nonlinearityofthedata. (4)DeterminationofthePVCpatterns,ifpresent,tocategorizedistincttypes
ofsinusarrhythmiasandNSR.WeusedtheMassachusettsInstituteofTechnology-BethIsraelHospital
(MIT-BIH) arrhythmia database, Creighton University’s VT arrhythmia database, the MIT-BIH
atrialfibrillationdatabase,andtheMIT-BIHmalignantventriculararrhythmiadatabasetotestour
algorithm. Binarydecisiontree(BDT)andsupportvectormachine(SVM)classifierswereusedin
bothstage1andstage3. Wealsocomparedourproposedalgorithm’sperformancetootherpublished
algorithms. Our VF detection algorithm was accurate, as in balanced datasets (and unbalanced,
inparentheses)itprovidedanaccuracyof95.1%(97.1%),sensitivityof94.5%(91.1%),andspecificity
of 94.2% (98.2%). The AF detection was accurate, as the sensitivity and specificity in balanced
datasets (and unbalanced, in parentheses) were found to be 97.8% (98.6%) and 97.21% (97.1%),
respectively. OurPVCdetectionalgorithmwasalsorobust,astheaccuracy,sensitivity,andspecificity
were found to be 99% (98.1%), 98.0% (96.2%), and 98.4% (99.4%), respectively, for balanced and
(unbalanced)datasets.
Keywords: automated arrhythmia classification; electrocardiography; health monitoring system;
prematureventricularcontraction;ventricularfibrillation;atrialfibrillation
1. Introduction
AccordingtotheCentersforDiseaseControlandPrevention,about735,000Americanssufferfrom
myocardialinfarctioneveryyear[1]. Heartdiseaseisoneoftheleadingcausesofdeath,asitaccounts
for25%ofmortalityintheUnitedStateseveryyear[2]. Itispossiblethatsomeofthesemortalities
can be prevented if malignant arrhythmias are detected early and accurately. With the advent of
wearable devices, which can facilitate personalized monitoring, continuous monitoring of cardiac
healthcannowberealized. Tothisend,moreaccurateandautomatedarrhythmiadetectionalgorithms
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basedonelectrocardiogram(ECG)signalsneedtobedeveloped. Arrhythmiascanbedividedinto
twomaingroups. Thefirstgroupofarrhythmiasisnotimminentlyfatalbutmayrequireproperly
diagnosedtherapytopreventmorphingintothesecondgrouporaseriouscomplication. Arrhythmias
inthesecondgroupneedimmediateattentionandtherapy. Amongthevariouskindsofarrhythmias,
ventricularfibrillation(VF)islifethreatening[3], whileatrialfibrillation(AF)isnot, butAFisthe
most prevalent arrhythmia and increases the risk of stroke [4,5]. Therefore, real-time monitoring
andaccuratediagnosisofmalignantarrhythmiaiscrucialsothattimelypreventivetreatmentscan
beperformed.
Medical devices, such as vital sign monitors, ECG recorders, and Holter monitors, are now
routinely available for collection of ECG data. However, other than implantable cardioverters,
mostarenotequippedwithautomatedarrhythmiaclassificationalgorithms[6]. Numeroustechniques
havebeenreportedforarrhythmiaclassification[6–8].Thesestudiesconsistofthreemajorphases:
preprocessing,extractingfeatures,andclassificationofvarioustypesofarrhythmiasineachECGdata
segment. ThemajorityofarrhythmiaclassificationisbasedonthevariabilityofR-Rintervalsorheart
ratevariability(HRV)analysis[9–11]. (Oneexceptionisasystole, whichhasnoRpeaks, sincethe
ECGisnearlyaflatline.) Morerecentstudieshavefocusedonclassificationofvariousarrhythmia
patterns[6,12–29]. Theextractedfeaturesforpatternclassificationhaveincluded: (1)timedomain
ormorphology-basedfeatures[15–17],suchasQRSdurationandamplitude,Pwavedurationand
amplitude,Twaveamplitude,STsegmentduration,orQTsegmentduration;(2)frequencydomain
features [6,18]; (3) nonlinearity features [19–23]; and (4) rhythm-based features [9–11]. Moreover,
combining features from various approaches has been shown to improve classification accuracy.
Forexample,wavelettransform(WT)featureshavebeenusedforclassificationofarrhythmiaswith
goodoutcomes[24–27]. However,someoftheissueswithWTarethechoiceofthepropermother
waveletfunction,appropriateselectionoftheorderofthefilter,andthelevelofsignaldecomposition.
Onenoteworthyrecentapproachisvariationalmodedecomposition(VMD),whichisbasedonthe
modificationoftheempiricalmodedecompositionmethod[13]. TheVMDmethodwasshownto
provide good classification results, including differentiation of normal (N), premature ventricular
contraction(PVC),left-bundlebranchblock(LBBB),right-bundlebranchblock(RBBB),prematurebeats
(PB),andatrialprematurecontraction(APC)beats. WhiletheVMDmethodwasshowntoprovide
goodclassificationresultsfortherhythmsnotedabove,itscapabilitytodifferentiatebetweenvarious
segments,suchasnormalsinusrhythm(NSR),VF,andventriculartachycardia(VT),wasnotprovided.
Webelieveourworkisoneofthefirstmethodswhichdoesnotassumethepresenceofaparticular
typeofarrhythmia. Figure1providesourcomprehensiveapproachtodetectingvariousarrhythmias.
We provide a comparison of our algorithm’s performance to the performance of some of the
standoutalgorithmswhichusedthesamepublicdatabaseswedid,consistingoftheMassachusetts
InstituteofTechnology-BethIsraelHospital(MIT-BIH)arrhythmiadatabase,CreightonUniversity’sVT
arrhythmiadatabase,theMIT-BIHatrialfibrillationdatabase,andtheMIT-BIHmalignantventricular
arrhythmiadatabase[30].
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4. ApplyinganRpeakdetectionalgorithmoneachnon-VFsegment;
4. Applying an R peak detection algorithm on each non-VF segment;
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noise;
• Converttophysicalunits,normalizeeachsignaltozeromeanandunivariance;
• Convert to physical units, normalize each signal to zero mean and univariance;
• Segmentthesignalinto8sdatalengthsandthenshiftthedataby3s,asthisprovidesthebest
• Segment the signal into 8 s data lengths and then shift the data by 3 s, as this provides the best
performance[31].
performance [31].
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2.2. Stage1/Step3: MethodologyofVFandNon-VFClassification
WhiletherearemanyalgorithmsavailableforautomateddetectionoftheonsetofVFarrhythmia,
there remains room for improvement of VF detection. We describe our approach to discriminate
betweenVFandnon-VFsegmentsusingsixfeatures,ofwhichfourarederivedfromimage-based
phase plot analysis, one is derived in the frequency domain, and the last reflects the nonlinear
characteristicsofadatasegment. Thesefeaturesaredescribedindetailbelow.
2.2.1. Image-BasedPhasePlotforMorphologicalAnalysis
Thephaseplot[32]isasimpletechniqueforcharacterizingnonlinearandnon-stationarydynamics
of the signal. This is a two-dimensional plot, which unfolds the phase dynamics of the signal if a
properly embedded time series in one axis is plotted against the current value of the signal in the
otheraxis. Wederivedfourimage-basedfeaturesaccordingtothedistributionandthemorphology
ofthephaseplot’strajectoriesforclassificationofVFandnon-VF(includingAF,NSR,andvarious
othertypesofsinusarrhythmias). Inthisstudy,weconstructedtwophaseplotsforeachECGsegment.
SupposewehaveasegmentS[s ,s ,s ,...,s ],wheres istheithsample. Inthefirstphaseplot,thex
1 2 3 n i
coordinateconsistsofthecurrentvaluesofsi,whereastheycoordinatecontainssi+1. Thesecondplot
visualizesarelationbetweensi versussi+5;thistimedelayof5sampleswaschosentofullyunfold
thedynamicsofthedata. Thus,foreverydatasegment,twophaseplotswithtwodifferenttimelags
arecomputed. Figure3showsarepresentativeNSRsegmentanditstwodifferenttime-laggedphase
plots. AsegmentofVFanditstwodifferenttime-laggedphaseplotsareshowninFigure4. While
NSRphaseplotsshoworganizeddistinctstructures,theVFphaseplotsshownot-well-definedand
irregulardynamics,especiallywiththedelayof5samplesthatencompassnearlyallofthephaseplot.
AsshowninFigures3and4,thelatterfigurereflectsthelessorganizedbehavioroftheVFsegmentin
comparisontotheNSRsegment.Amongvarioustypesofarrhythmias,VFhasthemostirregularphase
plot,characterizedbydisorganizedtrajectories. Phaseplotsofsinusarrhythmias,suchasBG,TG,QG,
couplet,triplet,andVT(especiallynon-sustainedVT(NSVT),whichisdefinedastheoccurrenceof
morethan3consecutivePVCs)aremoreorderedincomparisontoVF.Moreover,AFdatahavephase
plotcharacteristicssimilartoanNSRsegment,asshowninFigure5.
ToquantitativelydiscriminatebetweenVFandnon-VFfromthephaseplots,theyweresubsequently
transformedintobinaryimages, whichconsistofpixelsof1s(whitepixels)and0s(blackpixels)[33].
AstrajectoriesofVFphaseplotsaremorerandomlydistributedinthephasemap,itisexpectedthatthe
numberofwhitepixelsinthecorrespondingbinaryimageswillbemuchgreaterforVFthanfornon-VF
segments.Inordertoquantifythedifferencesinthenumberofwhiteandblackpixels,wedefineSasthe
totalnumberofpixelsineachimageandPasthenumberofcorrespondingwhitepixels(numberof1s).
Aself-similarityindexiscalculatedbytakingaratioofbinarypixelsinthefirstphaseplot(r1 =P1/S1)and
thesameratiointhesecondphaseplot(r2 = P2/S2)followedbysubtractionofthesetworatios(r2−r1).
Theself-similarityindexisusedasoneofthesixfeaturesanditisdenotedasF1henceforth.Itisexpected
thattheself-similarityindexvaluewillbesmallerforVFthanfornon-VF,asP(thenumberof1sinthe
binaryimage)willbegreaterforthelatter,asseeninthebinaryimagesinFigures3eand4e.Thesecond
feature,F2,representsthenumberoflinesinthephaseplot’sbinaryimagewhichareslanted45degrees
alongthediagonalline. Specifically,weassignavalueof1toalinethatisslanted45degreesalongthe
diagonallineforagivenpixel.Toensurethatalineissignificantlylarge,wehaveacriterionthatitmust
havealengthgreaterthan20pixels. AsshowninFigure6,somelinesarelongerthanothers,butthe
minimumlengthisgreaterthan20pixels. Thisthresholdwasdeterminedheuristically. TheVFphase
plotwould,ingeneral,haveahighernumberofthese45-degreeslantedlinesincomparisonwiththeSR
segments.F3reflectsthetotalnumberofpixelsthatareoccupiedbythelinesasdefinedforthefeatureF2.
Forexample,F3representsthesummationofthelengthsofF2lines.Thefourthfeature,F4,isextracted
through highlighting differences between the number of pixels that span the phase plot of a non-VF
segmentincomparisonwiththenumberofpixelsthatcoverthephaseplotofaVFsegment.Becauseofthe
disorganizedbehaviorofaVFphaseplot,theareaofwhitezoneintherelatedbinaryimageishigherthan
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a VF segment. Because of the disorganized behavior of a VF phase plot, the area of white zone in the
related binary image is higher than the area of white zone in a non-VF phase plot’s binary image (for
thinesatraenacoe,f cwohmitpeazroen Feiignuarenso 3nd-V aFnpdh 4adse). pAlost ’aslrbeinadaryy mimeangtieo(nfeodr inats ttahnec eb,ecgoimnnpianrge Foifg tuhriess s3edctainond, 4tdh)i.s
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Normal sinus rythm (NSR)
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(d) (e)
Figure 3. (a) An example NSR segment. (b) Phase plot of segment with a 1-sample lag. (c) Phase plot
Figure3.(a)AnexampleNSRsegment.(b)Phaseplotofsegmentwitha1-samplelag.(c)Phaseplot
of segment with a 5-sample lag. (d) Related binary image of (b). (e) Related binary image of (c).
ofsegmentwitha5-samplelag.(d)Relatedbinaryimageof(b).(e)Relatedbinaryimageof(c).
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(a)
1)
+
n
s(

(b) (c)

(d) (e)
Figure 4. (a) An example VF segment. (b) Phase plot of segment with a 1-sample lag. (c) Phase plot of
Figure4.(a)AnexampleVFsegment.(b)Phaseplotofsegmentwitha1-samplelag.(c)Phaseplotof
segment with a 5-sample lag. (d) Related binary image of (b). (e) Related binary image of (c).
segmentwitha5-samplelag.(d)Relatedbinaryimageof(b).(e)Relatedbinaryimageof(c).
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Atrial fibrillation (AF)
1.4
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(d) (e)
Figure 5. (a) An example AF segment. (b) Phase plot of segment with a 1-sample lag. (c) Phase plot of
Figure5.(a)AnexampleAFsegment.(b)Phaseplotofsegmentwitha1-samplelag.(c)Phaseplotof
segment with a 5-sample lag. (d) Related binary image of (b). (e) Related binary image of (c).
segmentwitha5-samplelag.(d)Relatedbinaryimageof(b).(e)Relatedbinaryimageof(c).
SSeennssoorrss2 2001188,,1 188,,2 x0 9F0OR PEER REVIEW 99o off2 255
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(a) (b)
(a) (b)
Figure 6. Binary image lines at 45 degrees in the phase plot (a) for an NSR segment (b) for a VF
Figure 6. Binary image lines at 45 degrees in the phase plot (a) for an NSR segment (b) for a VF
Figure6.Binaryimagelinesat45degreesinthephaseplot(a)foranNSRsegment(b)foraVFsegment.
segment.
segment.
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Morphological filling [34], which is an image-processing technique, means encompassing the
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holes (area of dark pixels surrounded by lighter ones) in a binary or grayscale image. Hence, after
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subtracting the first and second phase plot images of each segment, a filling operation is applied to
Sttheep srefsourlaticnqgu iismitiaogne.o Sfttehpiss ffeoart uarceqauriesiatisofno lolof wthsi:s feature are as follows:
the resulting image. Steps for acquisition of this feature are as follows:
••• MMMaaakkkeee t tthhheee b bbiiinnnaaarrryyy i iimmmaaagggeee o oofff t tthhheee fi ffirirrsssttt p pphhhaaassseee p pplloloottt w wwiitiththh d ddeeellalaayyyo oofff1 11f fofoorrre eeaaaccchhhs sseeegggmmmeeennnttt( (B((cid:1828)(cid:1828)1(cid:2869))))
••• MMMaaakkkeee t tthhheee b bbiiinnnaaarrryyy i iimmmaaagggeee o oofff t tthhheee s sseeecccooonnnddd p pphhhaaassseee p pplloloottt w wwiitiththh d ddeeellalaayyyo oofff5 55f fofoorrre eeaaaccchhhs sseeegggmmmeeennnttt(cid:2869)( (B((cid:1828)(cid:1828)2(cid:2870))))
••• SSSuuubbbtttrrraaacccttt B(cid:1828)(cid:1828)2(cid:2870)f rfforrmoommB 1(cid:1828)(cid:1828)((cid:2869)B 1(((cid:1828)(cid:1828)−(cid:2869) −−B 2(cid:1828)(cid:1828))(cid:2870))) (cid:2870)
••• AAApppppplllyyy ttthhheee fi (cid:2870)ffliillllilininnggg o oopppe(cid:2869)eerraraattit(cid:2869)oiioonnno oon(cid:2870)nnB (cid:1828)1(cid:1828)(cid:2869)−−− (cid:1828)(cid:1828)B(cid:2870)2... (((IIInnn fffaaacccttt,,, ttthhhiiisss ooopppeeerrraaatttiiiooonnn ccchhhaaannngggeeesss cccooonnnnnneeecccttteeeddd bbbaaaccckkkgggrrrooouuunnnddd
(cid:2869) (cid:2870)
ppiixxeellss( (00ss))t toof foorreeggrroouunnddp pixixeelsls( 1(1ss),),s stotoppppininggw whheenni titr ereaacchheesso obbjejectctb boouunnddaarireies.s).)
pixels (0s) to foreground pixels (1s), stopping when it reaches object boundaries.)
••• CCCooouuunnnttt t tthhheee n nnuuummmbbbeeerrr o oofff w wwhhhiititteee p ppiixixxeeellslss o oorrr t tthhheee n nnuuummmbbbeeerrr o oofff 1 11sss o oonnnt ththheeer rreeesssuuulltltitniinngggb bbiniinnaaarryryyi miimmaaagggeee...
For example, as shown in Figure 7, after the filling operation (which assigns 1 to holes and any
FFoorr eexxaammppllee,, aass sshhoowwnn iinn FFiigguurree 77,, aafftteerr tthhee fifilllliinngg ooppeerraattiioonn ((wwhhiicchh aassssiiggnnss 11 ttoo hhoolleess aanndd aannyy
other regions which are surrounded by 1) and counting the final number of white pixels (number of
ootthheerr rreeggiioonnss wwhhiicchh aarree ssuurrrroouunnddeedd bbyy 11)) aanndd ccoouunnttiinngg tthhee fifinnaall nnuummbbeerr ooff wwhhiittee ppiixxeellss ((nnuummbbeerr ooff
1s), the fourth feature is determined. Note that we used four connected background neighbors for the
11ss)),, tthhee ffoouurrtthh ffeeaattuurree iiss ddeetteerrmmiinneedd.. NNoottee tthhaatt wwee uusseedd ffoouurr ccoonnnneecctteedd bbaacckkggrroouunndd nneeiigghhbboorrss ffoorr tthhee
filling operation. We combined these for image-based features from the phase plot with two more
fifilllliinngg ooppeerraattiioonn.. WWee ccoommbbiinneedd tthheessee ffoorr iimmaaggee--bbaasseedd ffeeaattuurreess ffrroomm tthhee pphhaassee pplloott wwiitthh ttwwoo mmoorree
features (a frequency domain feature and Shannon entropy), which are described in the next section,
ffeeaattuurreess ((aa ffrreeqquueennccyy ddoommaaiinn ffeeaattuurree aanndd SShhaannnnoonn eennttrrooppyy)),, wwhhiicchh aarree ddeessccrriibbeedd iinn tthhee nneexxtt sseeccttiioonn,,
in order to develop a new method for better discrimination between VF and non-VF segments.
iinn oorrddeerr ttoo ddeevveelloopp aa nneeww mmeetthhoodd ffoorr bbeetttteerr ddiissccrriimmiinnaattiioonnb beettwweeeennV VFFa annddn noonn--VVFFs seeggmmeennttss..

(a) (b)
(a) (b)
Figure 7. Resulting images for acquisition of the fourth feature (a) for an NSR segment (b) for a VF
FFiigguurree 77.. RReessuullttiinngg imimaaggeess fofor raacqcquuisiistiitoinon ofo fthteh efofuoruthrt hfeafetuatrue r(ea)( af)orf oarna NnSNRS sRegsmegemnte (nbt) (fbo)r fao rVaF
segment.
VseFgsmegemnte. nt.
Sensors2018,18,2090 10of25
Sensors 2018, 18, x FOR PEER REVIEW 10 of 25
2.2.2. FrequencyDomainFeature
2.2.2. Frequency Domain Feature
Thefifthfeature,F5,forclassificationofaVFversusnon-VFsegment,isacquiredviaanalyzing
thesegmenTthien fitfhthe fferaetquuree,n Fc5y, dfoorm claaisnsi.fiScpateicoinfi coaf lal yV,Fw veeurssuesd naonp-oVwF esregsmpeecnttr,u ism actqouoirbetda ivniaa acnlaaslyszifiincga tion
the segment in the frequency domain. Specifically, we used a power spectrum to obtain a
featuretodistinguishbetweenVFandnon-VF.Figure8showssingle-sidedamplitudespectraforboth
classification feature to distinguish between VF and non-VF. Figure 8 shows single-sided amplitude
VFandnon-VFdatasegments. Eachdatasegmentconsistsof2400datapoints,andwechoseafast
spectra for both VF and non-VF data segments. Each data segment consists of 2400 data points, and
Fouriertransformdatalengthof2400sothatthefrequencyresolutionwas0.125Hz. Allamplitudes
we chose a fast Fourier transform data length of 2400 so that the frequency resolution was 0.125 Hz.
showninthisfigurearenormalizedtothemaximumamplitude. Inordertoquantifyeachsegment’s
All amplitudes shown in this figure are normalized to the maximum amplitude. In order to quantify
spectrum,wecalculatedthemeanspectralamplitudeovertheentireregion.
each segment’s spectrum, we calculated the mean spectral amplitude over the entire region.
AsshAows snhoinwFn iignu Friegu8r,et h8e, tmhee maneasnp sepcetrcatrlaal mamppliltituuddee ooff aa nnoonn--VVFF sesgemgmenetn ist issigsnigifniciafinctlayn htliyghheirg her
thanftohraan VfoFr as eVgFm seengmt.eTnht.i sThisise issp eescpieaclilayllyo bovbivoiouussi nin tthhee ffrreeqquueennccyy raranngeg eofo 0f–05–05 H0zH. Cz.ouCnotuinngt itnheg the
numbnerumofbferre oqfu ferenqcuieesncwiehs iwchhihcha vheavhei ghhigehrearm amplpiltiutuddeet thhaann tthhee mmeeaann vvalaulue,e i,t iits icslecalre athratth tahte tnhuemnbuemr ber
offreqouf efrnecqiueesnwcihesic whhhicahv ehaavhe iag hhiegrhaerm apmliptluitduedeth thaannt hthee mmeeaann vvaalluuee isi sggreraetaetre frorfo thret hneonn-oVnF- VthFant hfoarn for
VF segments. This can also be seen in Table 1 (feature F5) using the PhysioNet PhysioBank archive
VFsegments. ThiscanalsobeseeninTable1(featureF5)usingthePhysioNetPhysioBankarchive
datasets. These are described in Section 3.
datasets. ThesearedescribedinSection3.
Single-sided amplitude spectrum
1
non-VF
0.9
VF
0.8
0.7
0.6
S(f)|0.5
|
0.4
0.3
0.2
0.1
0
0 50 100 150
f(Hz)
Figure 8. Single-sided amplitude spectra of a non-VF segment (blue) and a VF segment (red).
Figure8.Single-sidedamplitudespectraofanon-VFsegment(blue)andaVFsegment(red).
Table 1. Mean ± standard deviation for each feature of the VF detection classifier.
Table1.Mean±standarddeviationforeachfeatureoftheVFdetectionclassifier.
Features Units Mean ± STD of Non-VF Mean ± STD of VF
FeatureFs1 UnRitastio Mean±0.S02T D± 0o.0f0N7 on-VF Me0a.0n4± ± 0S.T01D * ofVF
F1 F2 RNautimobers 0.70.231± ±0 4.0.2027 29.00.10 4± ±100.0.051 **
F2 F3 NumPbixeerlss 3570.3.615± ± 43.9202.05 142091..051 ±± 78160..40 5* *
F3 F4 PixPeilxsels 3500.6.057± ± 309.002.0 5 104.1021 .±5 0±.0748 *6 .4*
F4 F5 PNixuemlsbers 202.90.706± ±0 3.022.26 1570..51 2± ±280.3.034 **
F5 Numbers 229.06±32.26 157.5±28.33*
F6 Bits 0.60 ± 0.11 0.86 ± 0.05 *
F6 Bits 0.60±0.11 0.86±0.05*
* Denotes p < 0.05 based on Wilcoxon rank sum test, since data were found to be non-normal using
*Denotesp<0.05basedonWilcoxonranksumtest,sincedatawerefoundtobenon-normalusingtheone-sample
the one-sample Kolmogorov–Smirnov test.
Kolmogorov–Smirnovtest.
2.2.3. Nonlinear Feature (Shannon Entropy (SE))
2.2.3. NonlinearFeature(ShannonEntropy(SE))
The sixth feature, F6, is based on calculation of the Shannon entropy (SE) of the ECG signal. The
TShEe [3si5x] tohf sfiegantaulr Se ,isF d6e,fiinsebda asse: d on calculation of the Shannon entropy (SE) of the ECG signal.
TheSE[35]ofsignalSisdefinedas:
n
∑
H(S) = − p log p (1)
i 2 i
i=1