Automatic Detection of Atrial Fibrillation and Other Arrhythmias in ECG Recordings Acquired by a

14 Pages·2017·1.66 MB·English

electronics
Article
Automatic Detection of Atrial Fibrillation and
Other Arrhythmias in ECG Recordings Acquired
by a Smartphone Device
LuciaBilleci1,* ,MagdaCosti2,DavidLombardi2,FrancoChiarugi3andMaurizioVaranini1
1 InstituteofClinicalPhysiology,NationalResearchCouncilofItaly,viaMoruzzi1,56124Pisa,Italy;
[email protected]
2 CardiolineS.p.A.,viaLinz19-21-21,38121SpinidiGardolo(TN),Italy;[email protected](M.C.);
[email protected](D.L.)
3 DedalusS.p.A.ViaGaetanoMalasoma,24,56121Pisa,Italy;[email protected]
* 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:9August2018;Accepted:14September2018;Published:16September2018
Abstract: Atrialfibrillation(AF)isthemostcommoncardiacdiseaseandisassociatedwithother
cardiaccomplications. FewattemptshavebeenmadefordiscriminatingAFfromotherarrhythmias
and noise. The aim of this study is to present a novel approach for such a classification in short
ECG recordings acquired using a smartphone device. The implemented algorithm was tested
on the Physionet Computing in Cardiology Challenge 2017 Database and, for the purpose of
comparison,ontheMIT-BHAFdatabase. Afterfeatureextraction,thestepwiselineardiscriminant
analysis for feature selection was used. The Least Square Support Vector Machine classifier was
trainedandcross-validatedontheavailabledatasetoftheChallenge2017. Thebestperformance
was obtained with a total of 30 features. The algorithm produced the following performance:
F Normalrhythm=0.92;F AFrhythm: 0.82;F Otherrhythm=0.75;GlobalF =0.83,obtainingthe
1 1 1 1
thirdbestresultinthefollow-upphaseofthePhysionetChallenge. OntheMIT-BHADFdatabase
the algorithm gave the following performance: F Normal rhythm = 0.98; F AF rhythm: 0.99;
1 1
Global F = 0.98. Since the algorithm reliably detect AF and other rhythms in smartphone ECG
1
recordings,itcouldbeappliedforpersonalhealthmonitoringsystems.
Keywords: electrocardiogram;smartphone;atrialfibrillation;arrhythmias;supportvectormachine
1. Introduction
Atrialfibrillation(AF)isthemostcommoncardiacarrhythmiaandisassociatedwithseveral
complications. AccordingtotheGlobalBurdenofDisease, theprevalenceofAFisof33.5million
persons,affecting2.5%to3.2%ofpopulationworldwide[1].Severalstudiessuggestarisingprevalence
andincidenceofAF;itisexpectedthatAFwillaffect6–12millionpeopleintheUSAby2050and
17.9millioninEuropeby2060[2]. Possiblecausesofthisriseareenhanceddetection,increasingageing
ofthepopulation,andlifestylechanges[3,4]. AFisassociatedwithincreasedcardiovascularproblems,
especially stroke and heart failure, as well as increased mortality [5]. Thus, the detection of AF is
helpfultopreventsubsequentcomplications.
Academiaandindustryhaverespondedtothisneedwithanincreaseinresearchanddevelopment
regardingdevicesandstudiestowidenthechoicesforscreeningandmonitoringAF.
ThetraditionalcardiacmonitoringtechnologiesforAFdetection,liketheHolterdevice,haveone
or more drawbacks that limit the application of signal monitoring outside the clinics, including
laborious workflow, the need for specialized staff trained in arrhythmia detection, poor patient
compliance,cost,andinvasiveness. Recently,ECGdatahavebeencollectedusingnoveldeveloped
Electronics2018,7,199;doi:10.3390/electronics7090199 www.mdpi.com/journal/electronics
Electronics2018,7,199 2of14
technologies,includingsingle-leadECGadhesivesensors,implantablelooprecorders,smartphone
attachments,andwearables[6]. Recentadvancesinthesewearableandpointofcaredevicescould
potentiallyallowthedetectionofAFepisodesoutsideofclinicalsettingsinaneasyandcost-effective
way. Importantly, the computational power and the storage capacity of these devices allow the
executionofcomplexalgorithmsinrealtime, makingpossibletheonlinedetectionofAForother
cardiacepisodesusingstand-aloneapplicationsathome.
Different approaches have been adopted so far for AF detection, mainly using traditional
technologies. These approaches consist in inter-beat (RR) intervals analysis [7,8], time-frequency
analysisofECG[9,10],analysisbasedonPwaveabsence(PWA)andfrequencyspectrumanalysis
(FSA)[11,12]. AlgorithmsbasedonPwaveshavealowperformanceinthepresenceofnoiseasthese
wavesarepronetocontaminationwithmotionandnoiseartefacts[13]. Thus,mostofthemorerecent
approachesforAFdetectionarebasedonRRanalysis.However,somealgorithmshavebeendeveloped
combiningRRanalysisandanalysisofPWAand/orFSA,toenhancedetectionperformance[14–16].
Moreover,toimproveaccuracyandspecificityinAFdetection,severalpatternrecognitionapproaches
have been recently proposed including neural networks [17,18]. In particular, the Support Vector
Machine(SVM),anon-parametricclassifier,hasbeenemployedasitgivespromisingresultsinvarious
medicaldiagnostics[19]includingAFdetection[20,21].
Oneofthemajorlimitationsofthealgorithmsavailabletodateisthattheyareusuallyaimedonly
atdistinguishingAFfromnormalrhythmsandnotfromtheotherarrhythmias. Veryfewattempts
havebeenmadeinthisdirection. Carraraetal.[22]appliedaK-NN(K-NearestNeighborhood)to
classifyAF,normalsinusrhythm(NSR)andsinusrhythm(SR)withfrequentectopy,usingentropy,
localdynamics,andfractalscaling. Theauthorsachievedhighperformanceontheirowndatabaseof
2722patientswith24hECGrecordings(AF:97%,NSR:98%andSRwithectopy: 90%). Theauthors
also tested the algorithm on MIT–BIH databases obtaining lower performance for SR with ectopy
(40%)thussuggestingthatdynamicalmeasuresdidnotdistinguishatrialfromventricularectopy[23].
Entropy estimation was also applied to distinguish AF from ventricular tachycardia/ventricular
fibrillationobtaininganaccuracyof98%[24]. Uptonow,noneofthepublishedstudieshavetesteda
widesetofarrhythmiastoevaluatehowaccuratelytheycanbediscriminatedfromAF.
Recently,thePhysionet/ComputinginCardiologyChallenge2017encouragedthedevelopment
ofalgorithmstoclassifysingleshortECGleadrecordings,asNSR,AF,otherrhythm,ortoonoisytobe
classified[25]. Notably,theserecordingsareobtainedusingapointofcaredevice,AliveCorTM,anovel
smartphone connected ECG device coupled with an application to record and diagnose the ECG,
whichwaspreviouslydemonstratedtoprovideanaccurateandsensitivesingle-leadECGdiagnosisof
AF[26,27].
Inthispaper,weproposeanovelapproachfortheclassificationofsingle-leadshortECGrecording
as AF, normal rhythms, other type of rhythms and noise, by combining the three most important
characteristicsofAF,i.e.,RR,PWAandFSAfeatures. Amulti-classSVMwasthenapplied,forarobust
classification.TheproposedapproachwastestedonthePhysionetComputinginCardiologyChallenge
2017database[28]. ThedevelopedcodewassubmittedtothePhysionetOpen-SourceChallengecall
andisavailablefordownload(https://www.physionet.org/challenge/2017/sources/). Forthesake
of comparison, the algorithm was also tested on the MIT-BIH AF database (AFDB), consisting in
long-termECGsignalsacquiredusingambulatoryECGrecorders,althoughinthisdatabaseonlyAF
andNSRareannotated.
2. MaterialsandMethods
2.1. Datasets
The Physionet Computing in Cardiology Challenge 2017 database consists of a total of
12,186 ECG recordings. These data are single-channel signals obtained by the AliveCorTM Kardia
Electronics2018,7,199 3of14
smartphone connected device. Data were bandpass filtered (0.5–40 Hz) and stored at 300 sps,
with16bitprecisionanda±5mVdynamicrange.
Electronics 2018, 7, x FOR PEER REVIEW 3 of 14
The dataset was divided in a dataset, available to the public, for the development, training,
and validation of the algorithms and a hidden dataset, not available to the public, on which the
The dataset was divided in a dataset, available to the public, for the development, training, and
perforvmalaidnacteioon fotfh ethael gaolgroitrhitmhmssw aansde va ahluidadteend dbaytatsheet, Cnhoat llaevnagileaboler gtaon tihzee rsp.ubTlhice, oanv awilahbiclhe dthaet aset
consispteedrfoorfm8a5n2c8er eocf otrhdei nalggsolraitshtminsg wfraosm ev9aslutaote6d1 sbyw thhiele Cthhealhleindgdee nordgaantaizseertsc. oTnhtea ianveadila3b6l5e8 draetcaosredt ings
ofsimciolanrsilsetendg tohfs 8a5n2d8 crleacsosrddiinsgtrsi blausttiiongn sf.roTmhe 9r esc otord 6i1n gss wwheirlee ltahbe ehleidddaesn bdealotansgeti ncgontotaifnoeudr d36if5f8e rent
classerse:cNorSdRin,gAs Fo,fo stihmeirlarrh lyetnhgmth,s aanndd ncloaisssy driesctroibrdutiinognss.. AThftee rretchoerdoinffigcsi awleCreh laalbleenlegde acsl obseulorneg,itnhge rteo was
four different classes: NSR, AF, other rhythm, and noisy recordings. After the official Challenge
follow-up phase in which the annotations were reviewed and re-evaluated by experts to create a
closure, there was follow-up phase in which the annotations were reviewed and re-evaluated by
correctedversion(v3). FurtherdetailsaboutthePhysionetComputinginCardiologyChallenge2017
experts to create a corrected version (v3). Further details about the Physionet Computing in
databasecanbefoundinCliffordetal.[25].
Cardiology Challenge 2017 database can be found in Clifford et al. [25].
TheAFDBdatabase[29]includes25fullyannotatedECGrecordingsandatotalof299AFepisodes
The AFDB database [29] includes 25 fully annotated ECG recordings and a total of 299 AF
(mostlyparoxysmal). EachECGrecordingisapproximately10hlongandissampledat250Hz. Italso
episodes (mostly paroxysmal). Each ECG recording is approximately 10 h long and is sampled at 250
contaiHnzs. sIot malseoc caosnetsaionfs astormiael cflausettse orfa antrdiaNl fSluRt.ter and NSR.
InFigIun rFeig1utrhe e1 ftuhlel fpurlol pcerossceosfs tohf ethper porpoopsoesdedm meeththoodd iiss rreeppoorrtteedd. .
ECG Signals acquired using
Alivecordevice
SVM Classification
Learning: ten-fold cross
validation on 80% of the
available dataset
Feature extraction Feature selection
Pre-processing
RR, PWA, FSA Stepwise discriminant analysis
Validation: 20% of the
available dataset
RR series extraction and
correction
Normal rhythms
AF rhythms
Other rhythms
Noise

Figure1.Flowchartoftheproposedalgorithm.Abbreviationsare:Pwaveabsence(PWA),frequency
Figure 1. Flowchart of the proposed algorithm. Abbreviations are: P wave absence (PWA), frequency
spectrsupmectarnuamly asniasl(yFsiSsA (F),SaAt)r,i aatlrfiiabl rfiibllrailtliaotnion(A (AF)F.).
2.2. ECGPre-ProcessingandRRSeriesConstruction
2.2. ECG pre-Processing and RR Series Construction
ECGEsCigGn asligsnwales rweefirres ftirssut bsmubimttietdtedto tos osmomee pprree--pprroocceessssiinngg stsetpeps sini norodredr etro troedruecdeu ncoeisneo ainsed and
improivmepqrouvael iqtyu.aFliitrys.t F,iirmstp, iumlspiuvlesiavret iafratciftaccat ncacneclienligngw wasaso obbtataiinneedd bbyy ccoommppaarriningg ththe eECECG Gsigsniganl awliwth itha
mediaan mfieldteiarne dfilsteigrenda sli(g6n0alm (6s0w misn wdoinwdo):wt)h: ethEeC EGCGv avlaulueess wwhhoossee aabbssoolulutet edidffiefrfeenrecne cweitwh itthhe tfhilteerfieldte red
oneseoxnceese edxecededaetdh rae tshhroesldhowlde wreerree prelpaclaecdedw witihtht thhee aavveerraaggee oof fththe evavlauleuse bsebfoerfeo arenda nafdtear fttheermth [e3m0]. [30].
The resulting signal was then upsampled to 1200 Hz to increase the accuracy in the localization of
Theresultingsignalwasthenupsampledto1200Hztoincreasetheaccuracyinthelocalizationof
QRSs thus reducing the quantization of the RR intervals.
QRSsthusreducingthequantizationoftheRRintervals.
QRS detection was performed by applying a threshold on the absolute amplitude of a filtered
QRSdetectionwasperformedbyapplyingathresholdontheabsoluteamplitudeofafiltered
derivative signal. This threshold was updated at each new detection and was changed with the
derivative signal. This threshold was updated at each new detection and was changed with the
temporal distance from the previous QRS. The fiducial point of each QRS was selected as the time
temporaldistancefromthepreviousQRS.ThefiducialpointofeachQRSwasselectedasthetime
occurrence of the maximum (minimum) of the signed derivative signal. The beginning and the end
occurroefn QceRSo fwtehree mesatixmimatuedm b(ym thinei mcrousmsin)go fotfh deersiivganteivde dtherroivuagthiv tehes i0g.2n5a lt.hTrehsehobledg. iTnhniisn aglgaonridthtmhe foern dof
QRSwQeRrSe deestteimctiaotne dhabsy bteheen cprroesvsioinugsloy fpduebrliisvhaetdiv beyt horuor uggrohutph ean0d.2 o5btthairneesdh oal dve.rTyh giosoadlg poerriftohrmmafnocreQ RS
detect[i3o1n]. hTahse RbeRe snerpierse wviaosu tshleyn poubtbaliinsehde das bthyeo duifrfegrreonucep baentwdeoenb tsauicnceedssiavev eQrRySgs.o odperformance[31].
TheRRserieswasthenobtainedasthedifferencebetweensuccessiveQRSs.
2.3. Feature Extraction and Selection
2.3. FeatureExtractionandSelection
The extracted features can be categorized in four types: (1) derived from the RR series analysis;
(2) derived from PWA; (3) derived from FSA; and (4) other features derived from the ECG and QRS
Theextractedfeaturescanbecategorizedinfourtypes: (1)derivedfromtheRRseriesanalysis;
analysis. A set of 55 features was considered. The motivation for the selection of these features is that
(2)derivedfromPWA;(3)derivedfromFSA;and(4)otherfeaturesderivedfromtheECGandQRS
in clinical practice arrhythmia can be distinguished from normal heartbeats in terms of both
Electronics2018,7,199 4of14
analysis. Asetof55featureswasconsidered. Themotivationfortheselectionofthesefeaturesis
that in clinical practice arrhythmia can be distinguished from normal heartbeats in terms of both
morphologicalanddynamicdifferences. Indeed,arrhythmiaaretypicallyassociatedwithvarious
irregularitiesinheartrhythm,butcanusuallybecharacterizedalsobydifferentabnormalordistorted
patternsinthewaveformshape(e.g.,distortedQRScomplex)orbythemmissingsomeimportant
components (e.g., P wave). For this reason, we decided to use a combination of different types of
featuresfortherepresentationoftherhythm.
ANOVA was used for feature pre-selection and tuning (some features were log-transformed
in order to normalize their distribution and increase their discriminant power), then the stepwise
discriminantanalysiswithRao’Vcriterionforfeatureinclusionwasappliedtoselecttensetswithan
increasingnumberoffeatures. ThefinalsetoffeatureswasdeterminedbythevalidationofLS-SVM
classifiersasdescribedinSection2.4. The30finallyselectedfeatures,aswellastheirdiscriminant
power(F),arereportedinTable1. Inthefollowing,agenericdescriptionofthefeaturesisreported,
fordetailsseethesubmittedcode.
Table1.Descriptionoftheselectedfeatures.F:discriminantpower.
Feature F Description
FeaturesextractedfromtheRRseries
bigem 910 Bigemyrhythmindex
trigem 440 Trigemyrhythmindex
RRmean 250 MeanvalueoftheRRintervals
RRmax 168 MaxvalueoftheRRintervals
RRbrady 125 MeanvalueoftheRRintervalsabovethethresholdof1.2s
RRtachy 659 MeanvalueoftheRRintervalsbelowthethresholdof0.7s
CoSEn 1913 Coefficientofsampleentropy
Mawsdo 1771 Meanoftheabsoluteweightedsuccessivedifferencewithweightoptimization
Meanoftheabsoluteweightedsuccessivedifferencecontrollingforprematurebeats
Mawsdco 2114
withcompensatorypause(weightoptimization)
KFD 993 KatzFractalDimension
FrequencySpectrumAnalysis(FSA)
Poweraroundthepeakoftheweightedspectraldensityinthe3–20Hzband.
mPdAF 357
Thepowerofthethreeharmonicswasconsidered
mPdAFr 449 Ratiobetweenthepowerofthepeakandthepowerinthearound
Pwaveanalysis(PWA)
IndexofPwaveexistencebasedonthecomparisonbetweenamovingaverageof
P_numr 88
0.06swidthandamovingaverageof0.12swidth
IndexofPwaveexistenceobtainedcomparingamovingaverageof0.06swidthwith
Pexist 67 theoutputofacombfilterconsistingoftwomovingaverageof0.04swidth,
delayedby0.04s
IndexofPwaveexistenceobtainedcalculatingthepowerpeakswithinintervals
PintNr 240 beforetheQRSonsetandcomputingthemodeofthedistributionofthepositionsof
suchpeaks
OtherfeaturesextractedfromtheECGandtheQRScomplex
Powerofthesignalobtainedasthedifferencebetweentheoriginalandthelow-pass
pRhf 62
filtered(30Hz)signal(indexofnoise)
QRSwidthMeanQ 1101 Trimmedmean(2%taildiscard)oftheQRSwidthvalues
QRSwidthStdQ 545 Trimmedstandarddeviation(2%taildiscard)oftheQRSwidthvalues
QRSwidthMax3Q 619 MeanofthethreemaxvaluesoftheQRSwidth
QRSwidthMaxQs 292 Maxvalueofthemovingaverageon3valuesoftheQRSwidth
QRSppAmplMea 44 Trimmedmean(2%taildiscard)oftheQRSpeak-peakamplitude
RatiobetweentheQRSppAmplMeaandthepeak-peakamplitudeofthesignaloutside
QRSpprAmplMea 311
theQRSTinterval
Trimmedmean(3%taildiscard)oftheQRSpolaritycomputedasthesumofthe
QRSpolMea 26
maximumandtheminimumoftheQRSvalues
MinvalueoftheQRSequivalentwidths,definedastheQRSabsoluteareadividedby
QRSwidthMin 183
thepeak-peakamplitude
QRSwidthNmea 508 Trimmedmean(discarding40%ofhighvalues)oftheQRSequivalentwidths
PnWideQRS 149 RatiobetweenthenumberofQRSwithequivalentwidth>1.25×QRSwidthNmea
morphDiffr 550 MeanofthesumofabsolutedifferencesbetweentheQRSsandtheQRSmean
QRSsadV 19 Sumoftheabsolutedifferencesbetweenthetwofirstright-singular-vectors
nPVCp 353 Fractionoftheprematurebeatsthatarefollowedbyacompensatorypause
AtypBeatPrk 450 RatiobetweentherelativeQRSwidthandthebeatprematurity
Electronics2018,7,199 5of14
2.3.1. RRAnalysis
ThefeaturesextractedfromtheRRserieswerethemostdiscriminant. Thesubsetselectedby
step-wisediscriminantanalysisincluded: themeanandthemaxvalueoftheRRintervals;anindexof
tachycardia;anindexofbradycardia;andindexofbigeminyandanindexoftrigeminy;themeanof
theabsoluteweightedsuccessivedifference(Mawsd);thecoefficientofsampleentropy(CoSEn)[32]
andtheKatzFractalDimension(KFD)[33].
Forthecomputationofthebigeminyrhythmindex,wedefinedRRoddasthemeanofRRwith
oddindexandRRevenasthemeanofRRwithevenindex. Thebigeminyindexwasthencalculatedas
theabsolutevalueoftheratio: (RRodd−RReven)/(RRodd+RReven).
Forthecomputationofthetrigeminyrhythmindex,wedefinedRRmdasthemedianfiltered
RRusingawindowlengthof3values. Trigeminyindexwascomputedastheratiobetweenthesum
oftheabsolutevalueofsuccessivedifferencesofRRandthesumoftheabsolutevalueofsuccessive
differencesofRRmd.
ThefeatureMawsdwasbuiltwiththeaimofgettinganindexwhichtakesintoaccountmostly
theRRvariabilityduetoAF,excludingthatduetorespiratorysinusarrhythmiaandthatduetoother
arrhythmicevents. Maswdwascalculatedasthemeanofabsoluteweightedsuccessivedifferences
ofRRobtainedasfollows: (a)onlytheRRsuccessivedifferenceslowerthanahighthresholdvalue
andhigherthanalowthresholdvaluewereconsidered(thesethresholdsareproportionalthemedian
RR);(b)adifferentweightwasthenassignedtotheseRRdifferencesdependingonthesignchange
betweentwoconsecutiveRRdifferences. Moreover,aspecificcontrolaimedatexcludingthechanges
duetoprematurecontractionswithacompensatorypausewasintroducedinamodifiedversionof
thefeature(Mawsdc). Wealsocomputedanoptimizedversionofthesetwofeatures(Mawsdoand
Mawsdco). In this case, the initial values of the weights and thresholds which control the routine
behavior, were optimized by the Mesh Adaptive Direct Search (MADS) algorithm. We used the
toolbox“NOMAD”[34]withthetargettomaximizetheF scoreinAFdetectionusingasinglefeature
1
decisionrule.
2.3.2. PWaveAnalysis
The existence of a P wave was evaluated by applying a previously published algorithm [35].
Thealgorithmisbasedontwomovingaveragefilters(widths:0.06sand0.12s),followedbyadynamic
event-relatedthreshold. Amodifiedversionofthisalgorithmwasalsoapplied,whichusedamoving
averageof0.06swidthandacombfilterconsistingoftwomovingaveragesof0.04swidthdelayed
by0.04s. Inaddition,anindexofPwaveexistencewasalsoobtainedbyevaluatingthepowerofthe
signalinaspecificintervalbeforetheQRScomplex.
2.3.3. FrequencySpectrumAnalysis
The measures for the detection of the weak oscillation which characterize AF were extracted
byfrequencyspectrumanalysis. SincethehighQRSTpowermaycoverthisweakAFcomponent,
aQRSTcancellingprocedurewasapplied. ThiswasbasedonapproximatingeachQRSTbyPrincipal
ComponentmethodusingthepowerfulSingularValueDecomposition(SVD).Thesignalaroundeach
QRSwasweightedbyatrapezoidalwindowandstoredinthecolumnsofthematrixX,whichwas
decomposed by SVD. A matrix Xr was then rebuilt from the SVD decomposition using only the
eigenvectorsassociatedtothegreatest2or3eigenvalues. Theseeigenvectorscontainonlythesignal
componentswhicharepowerfulandsynchronous,thusthecolumnsofthematrixXrapproximate
theoriginalQRST.Asignal,containingmainlythecomponentwithventricularorigin,wasobtained
byunweightingtheestimatedQRSTsegmentsandconnectingthemwithastraightline. Thissignal
wassubtractedfromtheoriginalECG,obtainingaresidualsignalwhichretainstheAFcomponent.
Figure2shows,ontheleft,anexampleofQRSTcancellation(fromthetop:theoriginalsignal,theQRST
estimated component, and the residual signal where the oscillatory AF is enhanced), on the right,
Electronics2018,7,199 6of14
theestimateofthepowerspectraldensityobtainedbytheWelchmethodshowingthepeakduetothe
AFcoEmlecptroonnices n20t1.8, 7, x FOR PEER REVIEW 6 of 14
ThemeasuresrelatedtoAFextractedwere: thepowerofthesignalintheband4–10Hz;theratio
The measures related to AF extracted were: the power of the signal in the band 4–10 Hz; the ratio
betweenthepowerin4–10Hzbandandthepoweroutsidethisband; thepoweraroundthepeak
between the power in 4–10 Hz band and the power outside this band; the power around the peak of
ofaweightedspectraldensityinthe3–20Hzband(thepowerofthreeharmonicswasconsidered);
a weighted spectral density in the 3–20 Hz band (the power of three harmonics was considered); the
therartaitoiob beettwweeeenn tthhee ppoowweer roof fthteh epepaeka aknadn tdhet hpoewpeorw areoruanrdo.u nd.
1 −30
mV) 0.5
G ( 0 −35
EC −0.5
QRST (mV) −00..5501 D (dB/Hz) −−4450
e PS −50
V) 0.2
gn (m 0.10 −55
resS −0.1 −60
15 16 17 18 19 20 0 5 10 15 20 25 30
time (s) Frequency (Hz)

(a) (b)
FigurFeig2u.rRe e2c. oRredcoArd0 0A9000:9Q0:R QSRTSTca cnacneclellalatitoionn bbyy SSiinngguullaarr VVaalulue eDDeceocmompopsiotisointi o(SnV(DS)V (Da)) a(nad) aPnodwePro wer
spectsrpaelcdtreanl sditeyns(iPtyS D(P)SDof) roef sriedsuidaulasli gsinganlal( b(b).). eeQQRRSSTT:: eessttimimaatetde dQQRSRTS; Tr;esrSegsnS:g rne:sirdeusaidl usiaglnsail ganftaelr after
SVDScaVnDc ecallnacteiollnat.ion.
2.3.4.2O.3t.4h.e OrtFheera Ftuearteusres
FromFrtohme wthhe owlehoEleC EGCaGn ainn dinedxexo fofn nooisisee wwaass oobbttaaiinneedd aas sthteh epopwoewr eorf odfifdfeirfefnercee nbceetwbeeetnw teheen the
original signal and the 30 Hz low-pass pass filtered ECG (pRhf). From the QRS complex, measures
originalsignalandthe30Hzlow-passpassfilteredECG(pRhf). FromtheQRScomplex,measures
of QRS width, peak-peak amplitude, and polarization were extracted. Other features were obtained
ofQRSwidth,peak-peakamplitude,andpolarizationwereextracted. Otherfeatureswereobtained
combining QRS morphology measures (e.g., QRS width) and rhythm (e.g., prematurity). Descriptions
combiningQRSmorphologymeasures(e.g.,QRSwidth)andrhythm(e.g.,prematurity). Descriptions
of these features are reported in Table 1.
ofthesefeaturesarereportedinTable1.
2.4. Classification
2.4. Classification
In this study, we applied the LS-SVM (Least Square SVM) classifier [36], which is derived by the
Ionritghinisals StuVdMy ,[3w7]e. Aaps pLlSie-SdVtMh einLvSo-lSveVsM a l(eLaesta ssqtuSaqruesa rceosSt VfuMnc)ticolna swsiifithe req[u36al]i,tyw choincshtriasindtse,r tihvee d by
theosroigluintiaolnS cVanM be[ 3o7b]t.aiAnesdL bSy- SsVolMvinign av osylvsteesma olfe laisnteasrq euqauraetsiocnoss itnf uthnec ttriaonnsfworimthede qspuaacleit. yDceofinnisntgra ints,
thesoal uptoisointivcea nkebrneeol:b tainedbysolvingasystemoflinearequationsinthetransformedspace. Defining
apositivekernel: k(cid:0)kx(cid:3435)𝐱,(cid:2919)x,𝐱(cid:1)(cid:2920)(cid:3439)==ϕφ((x𝐱(cid:2919)))(cid:2904)Tφϕ(cid:3435)(cid:0)𝐱x(cid:2920)(cid:3439)(cid:1) (1) (1)
i j i j
the LS-SVM classifier formulation results as:
theLS-SVMclassifierformulationresultsas: (cid:2924)
y(x)=sign(cid:34)(cid:3429)(cid:3533)α(cid:2919)y(cid:2919)K(x,x(cid:2919))(cid:3433) (cid:35) (2)
n
y(x) =sign ∑(cid:2869)αyK(x,x) (2)
i i i
The radial basis function (RBF) was selected a1s kernel function K (·,·):
K(x,x)=exp[−‖x−x‖(cid:2870)⁄σ(cid:2870)] (3)
Theradialbasisfu(cid:2919)nction(RBF)w(cid:2919)asselectedaskernelfunctionK(·,·):
For the classification of the rhythms of the Physionet Challenge database, a multiclass model
(cid:104) (cid:105)
was adopted. The multiclass caKte(gxo,rxiz)at=ione xpprob−le(cid:107)mx −is xso(cid:107)l2v/edσ 2by a set of binary classifiers. We (3)
i i
adopted the “one-versus-one” coding [38], consisting in a set of binary classifiers, each discriminating
Fboetrwtheeenc tlwasos icfilacsasteiso.n oftherhythmsofthePhysionetChallengedatabase,amulticlassmodelwas
The LS-SVMlab toolbox [39] was used. First, the hyper-parameters (regularization γ and the RBF
adopted. Themulticlasscategorizationproblemissolvedbyasetofbinaryclassifiers. Weadopted
kernel dispersion σ2) were estimated and optimized by cross-validation (10-fold) selecting the values
the“one-versus-one”coding[38],consistinginasetofbinaryclassifiers,eachdiscriminatingbetween
which minimize the rate of misclassifications. Then, the parameters α and b of the SVM model were
twoclasses.
estimated. Overfitting of the dataset may occur and the estimated classifier may have poor
The LS-SVMlab toolbox [39] was used. First, the hyper-parameters (regularization γ and the
generalization capability. Therefore, we compared different LS-SVM classifiers using different
RBFkerneldispersionσ2)wereestimatedandoptimizedbycross-validation(10-fold)selectingthe
Electronics2018,7,199 7of14
values which minimize the rate of misclassifications. Then, the parameters α and b of the SVM
modelwereestimated. Overfittingofthedatasetmayoccurandtheestimatedclassifiermayhave
poorgeneralizationcapability. Therefore,wecompareddifferentLS-SVMclassifiersusingdifferent
numbersoffeatures,pre-selectedbystep-wisediscriminantanalysis. Suchclassifiersweretrained
onthe80%oftheavailabledatasetandthentheywerevalidatedontheremaining20%. Thefeature
setleadingtothebestperformanceonthevalidationsetwasselected. Sincecross-validationisused
tovalidatethehyper-parameterstotrainamodel,ratherthantovalidatethemodelitself,wethen
selectedthebestparameterstore-traintheselectedmodelonalltheavailabledataset. Thisallowedus
toreduceoverfittingusingahighernumberofcases.
ThefinalperformanceofthealgorithmwasthenassessedbytheChallengeorganizersrunning
thecodeonanindependenthidden“testset”,whichwasneverusedfortheclassifierestimation.
FortheAFDB,abinaryclassificationwasappliedastherhythmswereonlycategorizedasNSRor
AF.Inthiscase,theclassifierwastrainedonthe60%oftheAFDBandthenvalidatedontheremaining
40%. Inordertotestthegeneralizationcapabilitiesoftheapproach,thesamesetof30featuresselected
forthePhysionetChallengedatabasewasusedforAFDB.
2.5. Scoring
AccordingtothePhysionetChallenge2017scoringsystem[25],weevaluatedtheperformance
ofthealgorithmusingtheF measure,whichistheharmonicmeanbetweensensitivityandpositive
1
predictivevalue. ThefollowingnotationproposedinCliffordetal.[25]isused: thetrueclassesfor
Normal, AF, Other and Noisy rhythms are indicated by the uppercase letters “N”, “A”, “O” and
“P”respectively, thepredictedclassesareindicatedbythelowercaseletters“n”, “a”, “o”and“p”.
Thenotation“Xy”denotesthenumberofelementsoftheclass“X”assignedtotheclass“y”. TheF
1
valuewasobtainedforeachclassasfollows:
2×N
NSR:F = n (4)
1n ∑N+∑n
2×A
AFrhythms:F = a (5)
1a ∑A+∑a
2×O
Otherrhythms:F = o (6)
1o ∑O+∑o
2×P
p
Noisyrhythms:F = (7)
1p ∑P+∑p
Theglobalvaluewasthencalculatedasthemeanofthesinglevalues:
F +F +F
F = 1n 1a 1o (8)
1
3
Notably,theglobalscorewastheaverageofperformanceforNSR,AFrhythmsandotherrhythms
withtheintenttoemphasizecorrectclassificationofusablerecordings,reducingtheeffectiveweight
assignedtorecordingsthataretoonoisytobeanalyzed[25]. IntheAFDBonlyNSRandAFrhythms
werepresentandsotheF wascomputedas:
1
F +F
F = 1n 1a (9)
1
2
3. Results
3.1. PhysionetChallenge2017Database
Theresultsreportedhereafterrefertothelatestresultsachievedonthefinalsetofground-truth
labels(v3)ofthefollow-upphaseofthechallengeagreeduponbythechallengeorganizers,whichwere
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madeavailableinNovember2017. Indeed,theseannotationswerecorrectedandrevisedcomparedto
theofficialchallengeversionandsotheresultsaremorereliable.
The stepwise discriminant analysis selected 30 discriminant features, which are described in
Table1. Amongthese,theMawsdco(F=2114)andtheCosEn(F=1913)werethefeatureswiththe
Electronics 2018, 7, x FOR PEER REVIEW 8 of 14
mostdiscriminantpower,accordingtothemulticlassANOVAanalysis.
InIno rodredrert otou unnddeerrssttaanndd wwhhiicchh wweerree ththe emmosots stigsnigifnicifiancat nfetafteuarteus riens diinscdriimscirniamtiinnga teiancgh epaacihr opf air
ofcclalassssees,s ,eevvereyr ybbetewtweeene nclcalsassesse sAANNOOVAVA wwasa salaslos opeprefrofromrmede. dM. aMwaswdcsod choadh atdhet hmeamxiamxailm Fa liFn in
disdcirsicmriimnaintiantignNg SNRSRan adndA FArFh ryhtyhtmhms(sF (F= =1 01200280)8a) sassh sohwownnin inF iFgiugruere3 a3.aI.t Iits ips opsossisbilbeleto ton ontoictieceth tahtatth is
feathtuisr efeiastuurseu aisl luyshuiagllhye hriignhAerF inrh AyFth rmhysthcomms pcoamrepdawreidth wNitShR N.SR.

(a) (b)
(c) (d)

(e) (f)
FigFuigruer3e. H3. isHtoigstroagmrasmofs Mofe aMneoafnth eofa bthsoel uatbeswoleuitgeh twedeigsuhctecdes ssiuvcecdesifsfievree ndcieffceorenntrcoel licnogntfroorllpinregm faotru re
bepatrsemwaitthurceo mbepaetsn swaittohr ycopmaupseens(wateoirgyh ptaoupsteim (wizeaitgiohnt )o(pMtiamwizsadtcioon))( a()Maanwdscdoceof)fi (caie) natnodf csoaemffpicleieennt torof py
(CsoaSmEpnl)e( ebn)tfroorpyN (aCnodSEAn)r (hby)t fhomr sN; hainsdto Ag rrahmytshomfsC; hoiSsEtongr(ca)masn odf MCoaSwEsnd (cco) a(dnd) fMoraOwsadncdo (Ad)r hfoyrt hOm s;
hisatnodg rAam rhsyothfmroso; thmisetoagnrasmqus aorfe roofostu mcceeasns isvqeudarifef eorfe nsuceccse(sRsiMveS SdDiff)e(ree)nacensd (KRMatSzSFDr)a c(eta) laDndim Keantszi on
(KFFrDa)ct(afl) DfoimrNenasniodn O(KrFhDy)t h(fm) fso.rN N: nanodrm Oa rl;hAyt:hamtrsi.a Nlfi: bnroirlmlatailo; nA;:O at:roiathl feirb.rillation; O: other.
OnOeniem impoprotratnantta aimimo offt hthiiss ssttuuddyy wwaass ttoo aaccccuurraatteellyy ddisisccrirmimininataet eAAF Ffrformom otohtehr errhryhthymthsm ass athset he
literature on this topic is still poor. According to the ANOVA, the CoSEn (F = 2563) and the Mawsdco
literatureonthistopicisstillpoor. AccordingtotheANOVA,theCoSEn(F=2563)andtheMawsdco
(F = 1933) were the features with the most discriminant power for the classification between these
two classes as observed in Figure 3c,d. Both the two features resulted generally higher in AF rhythms
compared with other rhythms.
For the discrimination between normal and other rhythms, RMSSD results as the most powerful
in the discrimination (F = 1712) followed by KFD (F = 1551) as shown in Figure 3e,f. Although there
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(F=1933)werethefeatureswiththemostdiscriminantpowerfortheclassificationbetweenthese
twoclassesasobservedinFigure3c,d. BoththetwofeaturesresultedgenerallyhigherinAFrhythms
comparedwithotherrhythms.
Forthediscriminationbetweennormalandotherrhythms,RMSSDresultsasthemostpowerful
inthediscrimination(F=1712)followedbyKFD(F=1551)asshowninFigure3e,f. Althoughthereis
awindowofoverlapbetweenthetwoclasses,otherrhythmshaveoftenahigherRMSSDcompared
withNSR.Itshouldbenotedthat,despiteitsdiscriminantpowerindifferentiatingbetweennormal
andotherrhythms,theRMSSDwasnotselectedbythestepwisediscriminantanalysis,asprobablyits
discriminantinformationwasredundantconsideringtheotherincludedfeatures.
Inallthefigures,theareasofthehistogramsforeachclassarenormalizedtoone,soareestimates
oftheclass-conditionalprobabilitydensities.
Usingthe30selectedfeatures,theLS-SVMclassifierprovidedthefollowingperformanceinthe
multi-classclassificationonalltheavailabledatasetoftheChallenge(afterthere-trainofthemodel):
F =0.93;F =0.85;F =0.83andF =0.87. FornoisyrecordingswegotF =0.83. Theconfusion
1n 1a 1o 1 1p
matrixobtainedisreportedinTable2.
Table2.ConfusionmatrixontheavailablesetofthePhysionetComputinginCardiologyChallenge
2017.N:normal,A:atrialfibrillation,O:otherrhythms,P:noisy.Lowercaselettersdenotetheclassifier
predictedclasses.Thebackgroundgreencolorindicatescorrectclassifications.
n a o p
N 4923 16 129 8
A 23 635 95 5
O 459 74 1875 6
P 58 7 3 108
Onthehiddentestofthefollow-upphasetheperformancewas:F =0.919;F =0.824;F =0.746
1n 1a 1o
andF =0.83. Withthisperformanceweobtainedthethirdplaceoutof84participantsinthefinalrank
1
ofthefollow-upchallenge(firstplace: F =0.921;F =0.857;F =0.766andF =0.848[40];second
1n 1a 1o 1
place: F =0.908;F =0.841;F =0.745andF =0.832[41](https://groups.google.com/forum/
1n 1a 1o 1
#!topic/physionet-challenges/qA2iUfQmRtc). Acomparativesummaryofthealgorithms[40–43],
whichobtainedthetopfivescoresinthefollow-upphaseofthePhysionetChallenge2017,isreported
inTable3.
Table3.Comparisonoftheresultsoftheproposedmethodswiththatoftheotheralgorithmsproposed
forthePhysionetChallenge2017obtainedinthefollow-upphase.
Reference NumberofFeatures ClassificationMethod OverallPerformance
GradientBoostingclassifierand
Teijeiroetal.[40] 42 RecurrentNeuralNetwork F1=0.848
Kropfetal.[41] 380 RandomForest F1=0.832
Billecietal.(proposedmethod) 30 LeastSquare-SupportVectorMachine F1=0.830
Dattaetal.[42] 150 Multi-layerCascadedBinary F1=0.8294
Plesinger[43] 60 NeuralNetwok+BaggedTreeEnsemble F1=0.8278
Notably, ourperformanceonthehiddentestsetoftheofficialchallengephasewas: F1n =0.911; F1a =0.784;
F1o =0.739andF1 =0.812(obtainingthe12thplaceintheofficialrankinglist). Theincreaseinperformance
achievedfromtheofficialphasetothefollow-upphasesuggeststheimportanceofhavingreliableannotationsfor
thealgorithmtrainingandevaluation.
3.2. MIT-BHAFDatabase
TheapplicationofthebinarySVMclassifierontheAFDBprovidedthefollowingperformance:
F Normalrhythm=0.98; F AFrhythm: 0.99; GlobalF =0.98. Theconfusionmatrixobtainedis
1 1 1
reportedinTable4.
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Table4.ConfusionmatrixontheAFDB.N:normal,A:atrialfibrillation;lowercaselettersdenotethe
classifierpredictedclasses.Thebackgroundgreencolorindicatescorrectclassifications.
n a
N 5393 75
A 80 10,937
4. Discussion
In this study, we proposed a novel approach for discriminating between normal, AF,
other rhythms, and noisy ECG records. The algorithm achieved high global performance both in
short-term(F =0.83)andlong-termrecordings(F =0.98).Importantly,theresultsofthisstudyshowed
1 1
the possibility of accurately detecting and discriminating AF in single short-lead ECG recordings
obtainedwithasmartphone-connecteddevice. Inparticular,ascanbeobservedfromtheconfusion
matrix,theclassifierseemsparticularlyefficientindiscriminatingbetweennormalAFrhythmsandAF
fromotherrhythms,whileitislessefficientindiscriminatingnormalfromotherrhythms,asshownin
Table2. Veryfewattemptshavebeenmade,outsidethePhysionetChallenge2017,todiscriminateAF
fromtheotherarrhythmia. Thus,theclassificationofdifferenttypesofrhythmsisanimportantnovel
aspectofthealgorithms,includingours,implementedforthechallenge.
Comparedtotheotheralgorithmssubmittedforthechallenge,thestrengthofourapproachisthat
itusedaquitesimplemethodforextractingtemporalandspectralfeaturesandapplyingtheLS-SVM
forclassification. Thisallowedustoobtainagoodtrade-offbetweenaccuracyandcomputationaltime,
thuspotentiallyallowingareal-timeapplicationofthealgorithm. Moreover,weusedalownumberof
featurescomparedtotheotheralgorithms,asseeninTable3,whichfosteredagoodgeneralization
capabilityofthealgorithm.
Notably,theproposedalgorithmreliesonthecombinationofdifferenttypesoffeatures,obtained
by RR analysis, PWA, and FSA for a better characterization and discrimination of AF. Moreover,
other features extracted from the ECG or specifically from the QRS have been computed in order
tobetterdiscriminateAFfromotherrhythms. Fewpreviousstudieshaveusedasimilarcombined
approachforabinaryclassificationofAFandNSRobtaininghighperformance[14,15]. Theresults
of our study suggest that a similar multidimensional approach can be extended to the multiclass
problem. Indeed,accordingtotheresultsofacomparativestudyofAFdetectionalgorithms,theones
thatcombineRRandatrialactivity(AA)analysisarethosewiththehighestpositivepredictivevalue,
sowithbothahighspecificityandsensitivity[13]. Thisisrelevantwhentheaimisnotonlytodetect
AFbutalsotodistinguishitfromotherrhythms.
According to the ANOVA analysis, the features extracted from the RR series were the most
discriminantones. Indeed,theAFdoesnotconveytotheventriclestherhythmofthesinusnode,
butirregularstimulationduetotherecurrentpropagationofthedepolarizationwaveontheatria.
Thus,theirregularityofAFmostlyaffectstheRR.Inaddition,theAAcanbeinfluencedbynoise[13]
limitingthereliabilityoftheFSAfeatures.
Inparticular,theMawsdcoandtheCoSEnwerethemostdiscriminantfeaturesfordistinguishing
among the different classes. CoSEn also resulted as one of the two most discriminant features in
classifyingNSRandAFrhythms. ThehighefficiencyofCoSEnindetectingAFconfirmsprevious
studies[18]. Indeed,duringAF,electricaldischargesconductedfromtheatriumintotheventricles
are irregular and disorganized so that randomness and entropy increase during an AF episode.
Inparticular, ithasbeennoticedthatCoSEnisparticularlyrelevantwhenclassifyingshortlength
recordings [32,44]. Importantly, we observed that CoSEn is also the most powerful feature in the
discriminationbetweenAFandotherrhythms.Thefewstudieswhichattempttodiscriminatebetween
AF and other type of rhythms showed the good discriminant power of this feature [22–24]. Thus,
ourstudyconfirmstheimportanceofincludingthisfeaturetodiscriminateAFfromotherrhythms.