Arrhythmia Evaluation in Wearable ECG Devices

14 Pages·2017·2.91 MB·English

sensors
Article
Arrhythmia Evaluation in Wearable ECG Devices
MuammarSadrawi1,Chien-HungLin2,Yin-TsongLin2 ID,YitaHsieh2,Chia-ChunKuo2,
JenChienChien2,KoichiHaraikawa2,MaysamF.Abbod3andJiann-ShingShieh1,*
1 DepartmentofMechanicalEngineeringandInnovationCenterforBigDataandDigitalConvergence,
YuanZeUniversity,Taoyuan,Chung-Li32003,Taiwan;[email protected]
2 HealthcareandBeautyRDCenter,KinpoElectronics,Inc.,NewTaipeiCity222,Taiwan;
[email protected](C.-H.L.);[email protected](Y.-T.L.);[email protected](Y.H.);
[email protected](C.-C.K.);[email protected](J.C.C.);[email protected](K.H.)
3 DepartmentofElectronicandComputerEngineering,BrunelUniversityLondon,UxbridgeUB83PH,UK;
[email protected]
* Correspondence:[email protected]
Received:19September2017;Accepted:21October2017;Published:25October2017
Abstract: Thisstudyevaluates fourdatabasesfrom PhysioNet: TheAmericanHeartAssociation
database(AHADB),CreightonUniversityVentricularTachyarrhythmiadatabase(CUDB),MIT-BIH
Arrhythmiadatabase(MITDB),andMIT-BIHNoiseStressTestdatabase(NSTDB).TheANSI/AAMI
EC57:2012isusedfortheevaluationofthealgorithmsforthesupraventricularectopicbeat(SVEB),
ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the
evaluationofthesensitivity,positivepredictivityandfalsepositiverate. Sampleentropy,fastFourier
transform(FFT),andmultilayerperceptronneuralnetworkwithbackpropagationtrainingalgorithm
areselectedfortheintegrateddetectionalgorithms. Forthisstudy, theresultforSVEBhassome
improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further,
VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the
positivepredictivityoftheNSTDBdatabase. ForAFgrossevaluationofMITDBdatabase,theresults
showverygoodclassification,excludingtheepisodesensitivity. Inadvanced,forVFgrossevaluation,
theepisodesensitivityandpositivepredictivityfortheAHADB,MITDB,andCUDB,havegreater
than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results
show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate
classificationaccordingtoANSI/AAMIEC57:2012. Inconclusion,theproposedintegrateddetection
algorithm can achieve good accuracy in comparison with other previous studies. Furthermore,
more advanced algorithms and hardware devices should be performed in future for arrhythmia
detectionandevaluation.
Keywords: wearable sensor; arrhythmia; sample entropy; fast Fourier transform; artificial
neuralnetworks
1. Introduction
Nowadays,wearablesensor-basedsystemhasbeenappliedtowideapplications. Fallandactivity
monitoringstudywithutilizingwearablesensorhasbeenconductedbyShanyetal.[1]. Thiskind
of system was also used for Parkinson’s disease with the combination of Support Vector Machine
(SVM)byastudyconductedbyPateletal.[2]. Meanwhile,Corbishleyetal. usednon-invasiveand
continuouswearablesystemforbreathingmonitoring[3]. Furthermore,wearableelectrocardiogram
(ECG)devicewasalsoutilizedfortheemotionclassificationsviahearratevariability[4].
Recently,theimplementationofintensivelyevaluatedECGsignalthroughwearablesensorisone
oftheessentialissuesforthecardiovascular-relateddiseases. Forexample,arrhythmiahasbeenoneof
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Sensors2017,17,2445 2of14
theconcerningcardiacdiseases. Someofthearrhythmiacasesareclassifiedaslife-threateningevents.
Therefore,Rosenbergetal. utilizedlong-termmonitoringsystemforatrialfibrillationmonitoring[5].
AstudybyBaigetal. evaluatedwearableECGsystemforolder-adultpopulation[6]. Furthermore,
Fenslietal. utilizedwearablesensorforarrhythmiadetectionappliedtotele-homecaresystemvia
generalpacketradioservice(GPRS)topersonalcomputerasbasestation. Thisinformationwillbe
evaluatedbydoctorswithremotesystemfortherhythmevaluationbytheinternetconnectionserver[7].
Linetal. developed an intelligent telecardiology system for sinus tachycardia, sinus bradycardia,
wideQRScomplex,atrialfibrillation,andcardiacasystole[8]. Thissystemcanalsoactivateemergency
alarm. In advanced, Hu et al. had successfully applied hidden Markov model to using wearable
systemforECGarrhythmiaevaluation[9]. Meanwhile,recentstudybyHadiyosoetal. alsoconducted
astudyonarrhythmiadetectionviasmartphone[10].
Atrial fibrillation (AF) and ventricular fibrillation (VF) are frequent arrhythmias. The former,
AF,isoneofthearrhythmiasrelatedtoageandhasseriouseffectonmorbidity,mortality,andcost[11].
AFalsoisanindependentfactorandhassignificanteffectontheriskofstrokebyastudyconducted
byWolfetal. onfivethousandcasesbothfemaleandmaleformorethanthirtyyears[12]. Karaetal.
utilized power spectral density and Daubechies wavelets with backpropagation artificial neural
network (ANN) for AF detection [13]. Roonizi et al. used extended Kalman filter to evaluate AF
frequency[14].AstudybyMohebbietal.investigatedAFbyapplyingthefeaturedimensionreduction
techniquewithSVMclassifier[15]. Abdul-Kadiretal. useddynamicECGsystemaccordingtosecond
orderdifferentialequationofECGbehaviorwithcrossvalidationtechniquebyutilizingSVMand
ANNaspredictors[16]. Recently,Rajpurkaretal. haveutilizedoneofthedeeplearningtechniques,
namelya34-layerconvolutionalneuralnetworkfordetectingarrhythmia,includingAF[17].
Another arrhythmia is VF. According to McWilliam, VF has strong correlation with sudden
cardiac diseases [18] and it is critical to defibrillation [19]. Alonso-Atienza et al. have applied
bootstrapresampling-basedfeatureextractionSVMclassifierforVFdetection[20].AstudybyAnas
etal. describeshowempiricalmodedecompositionmethodisusedtodiscriminateVFandnon-VF
rhythms[21].
Atrialprematurecomplex(APC)andventricularprematurecomplex(VPC)areotherfrequent
arrhythmias that, according to the ANSI/AAMI EC57:2012, can be classified as supraventricular
ectopicbeat(SVEB)andventricularectopicbeat(VEB).Researchondetectingtheseconditionsand
other arrhythmias have been in several previous studies. Thong et al., have utilized paroxysmal
atrialfibrillationforAPCcalculation[22]. Sayadietal. haveusedextendedKalmanfilterforVPC
detection[23]. Similarly,astudybyÖzbayetal. thatevaluatedtheperformanceoftheneuralnetworks
alsoperformedtheinitializationoffuzzyC-meansforAPC,VPC,andotherECGarrhythmia[24].
Songetal. utilizedsupportvectormachinewiththecombinationofdimensionalityreductionusing
principalcomponentanalysis,andlineardiscriminantanalysisforarrhythmiaclassifierincludingAPC
andVPC.Theyfoundbetterresultascomparedtomultilayerperceptron(MLP)andfuzzyinference
system[25].
However,thesepreviousstudiesarerelativelycomputationallycomplextobeappliedtowearable
devices.Severalstudies,whicharerelativelylesscomputationalcomplexity,performedwellforAFand
VFdetection.ForAFdetection,Zhouetal.proposedapowerfulalgorithmforrealtimedetectionofAF.
Thisstudyevaluatedtheheartratetocreateasymbolicandwordsequences. Inadvanced,Shannon
entropywasutilizedtoevaluatethewordsequenceinordertoclassifyAF[26]. Ontheotherhand,
FFTalgorithmhasbeenarobustalgorithmforsignaldetectionalgorithminrecentstudies[27–30]
and has been effectively applied for VF detection for decades. For VF, Clayton et al. evaluated
signalspectrumbyutilizingfastFouriertransform(FFT)andmaximumentropyforVFdetection[31].
Afonsoetal. appliedshorttimeFouriertransform(STFT),smoothedpseudoWigner-Villedistribution
andcone-shapedkernelfortheVFevaluation[32]. Recently,wearablesensor-basedsystemhasbeen
widelyappliedandutilized,thechanceofthereal-timeevaluationforthearrhythmiadetectionwith
theless-complicatedalgorithmsishighlylikelyanacceptableinvestigation. Hence,themainpurpose
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tShenes orasr2r0h1y7,th17m,2i4a4 5detection with the less-complicated algorithms is highly likely an accep3taobfl1e4
investigation. Hence, the main purpose of this study is to efficiently apply less complexity
algorithms for real-time detection of arrhythmias utilizing wearable devices based on ANSI/AAMI
ofthisstudyistoefficientlyapplylesscomplexityalgorithmsforreal-timedetectionofarrhythmias
EC57:2012 evaluation.
utilizingwearabledevicesbasedonANSI/AAMIEC57:2012evaluation.
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Table1.TheBC1devicespecification.
CMRR(Common-moderejectionratio) 80dB(dcto60Hz)
Highsignalgain (G=100)withdcblockingcapabilities
Single-supplyoperation 2.0Vto3.5V
ADC(Analog-to-DigitalConverter) 12-bit
InputImpedance 5GigaOhm
The integrated evaluation is started by evaluating the APC, VPC, AF, and VF based on
ANSI/AAMIEC57:2012,asshowninFigure2.Originally,thedatadownloadedfromPhysioNet[34–37]
isdownsampledto200Hz.Initiallythe5-minECGsignalisevaluatedeitherbasedonthebeat,forVPC,
AF,andAPCevaluationsortherawECGsignal,the2-swindow,forVFdetection.
VPCevaluationisinitiatedbytheR-Rintervalevaluation. Theevaluationiscalculatedbased-on
the study by Hamilton et al. [39]. Another study by Hamilton [40] is utilized for VPC evaluation,
as shown on Figure 2A. For AF calculation, the previous evaluation of VPC beat is essentially
important. Theabnormalbeats(i.e.,VPCbeats)willimitatetheR-Rintervalinthenormalrhythm.
ThisphenomenonhighlylikelyincreasestheuncertaintyinclassifyingeithernormalorAFrhythm.
PreviousclassificationresultsfordetectingVPCwillbeutilizedtoreformtheoriginalR-Rintervalby
averagingthepreviousbeatandthenextbeat. TheAFevaluationisoriginallycalculatedbasedon
Zhouetal. study[26]. TheheartrateiscalculatedfromtheoriginalR-Rinterval. Thisheartrateis
convertedtosymbolicsequenceusingEquation(1). Furthermore,thissymbolicsequenceisutilized
forthewordvaluebyEquation(2)asshownbythefollowing:
(cid:40)
63 if hr ≥315
n
sy = (1)
n
(cid:98)hr /5(cid:99) othercases
n
(cid:16) (cid:17) (cid:16) (cid:17)
wvn = syn−2 x212 + syn−1 x26 + syn (2)
wherehristheheartrate,sy isthesymbolicsequence,andwv isthewordvalue. Thiswordvalue
n n
sequence evaluation, originally calculated using Shannon entropy, is replaced by sample entropy
algorithm[41]. AFevaluationcanbeseeninFigure2B.
ForAPCdetection,themorphologicalECGisutilizedforfeatureextractionandartificialneural
networks. Multi-layerperceptronwithbackpropagationtrainingalgorithmandsinglehiddenlayer
isutilized. FeaturesfortheANNinputextractedfromECGsignalareP-Rinterval,QRSduration,
R-Rinterval,nextR-Rinterval,average,andstandarddeviationofR-Rintervalof10beatsbeforeand
afterthecurrentbeat,andR-waveamplitude,asshowninFigures2Cand3,whichisbasedonour
previousstudy[42]. Thedataisdividedinto60%fortraining,20%forvalidation,and20%fortesting.
After R-R interval-based algorithm is performed, the raw ECG signal-based evaluation is
calculated. Theinitial5-minECGsegmentisreshapedtoseveral2-sECGsignals. Thisevaluationis
organizedtoavoidmixedrhythmsfortheclassification. Theperiodogramevaluationisutilizedby
findingitsmaximumpointcorrespondingtothefrequencyoftheshortenedECGsegments. According
toLoetal.,thedominantVFwaveformfrequencyisbetween1Hzand7Hz[43]. Ourstudyutilizes
asimilarrangewithsomeoffset. Threefocusedareaofmaximasaredefined. ThefirstoneistheVF
area(p_vf). Thisareaislocatedinbetweenthefrequencyofgreaterthanequalsto2.61Hzandless
thanequalsto4.95Hz. Thenextareaisthefirstareaofnon-VF(p_nVF),whichisbetweenfrequency
greater than 0.5 Hz and less than 2.61 Hz. The last area is the second non-VF (p_nVF2), which is
locatedbetweenfrequencygreaterthan4.95Hzandlessthanorequalsto10Hz. Theratioofthep_vf
tothesummationofthep_nVFandp_nVF2isdefinedinordertoclassifyeithernormalorVFrhythm.
Thethresholdoftheratioisfixedto3.96. ThedetailedevaluationoftheVFarrhythmiacanbeseenin
Figure2D.
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SSeennssoorrss 22001177,, 1177,, 22444455 55 ooff 1144

Figure 2. Integrated arrhythmia evaluation flowchart; (A) Ventricular premature complex detection;
Figure 2. Integrated arrhythmia evaluation flowchart; (A) Ventricular premature complex
Figure 2. Integrated arrhythmia evaluation flowchart; (A) Ventricular premature complex detection;
(B) Atrial fibrillation detection; (C) Atrial premature complex detection; (D) Ventricular fibrillation
detection; (B)Atrialfibrillationdetection; (C)Atrialprematurecomplexdetection; (D)Ventricular
(B) Atrial fibrillation detection; (C) Atrial premature complex detection; (D) Ventricular fibrillation
detection.
fibrillationdetection.
detection.

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detection algorithm.
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3. Results
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Prior to PhysioNet database evaluation, Fluke simulator data of Normal sinus rhythm, APC,
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VPC, AF, and VF are utilized from BC1 for algorithm evaluation on smartphone in real-time
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condition. Besides, visualizing real-time signals with its signal annotation are shown in Figure 4. For
AVPPCC, adnedte cVtiPoCn, idseetveacltuioante dis beavsaeldu-aotnedth beaRse-wd-aovne tohfet hRe-wECavGes oigf ntahle. MECeaGn wsighnilae,l.n Moremanalwshinilues, rnhoyrtmhmal,
APC and VPC, detection is evaluated based-on the R-wave of the ECG signal. Meanwhile, normal
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sinus rhythm, AF and VF evaluation works based-on a segment. The mobile application is also able
tdoo sctuomree tnhtee ddoscigunmaelnwteitdh siitgsnsaigl nwailtha nitnso stiagtnioanl aansntohteatoifofn-l ianse tehvea oluffa-ltiinoen erveacoluradtsiotnh arteccoanrdbs ethsaete ncainn
to store the documented signal with its signal annotation as the off-line evaluation records that can
bFeig suereen5 i.n Figure 5.
be seen in Figure 5.
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Figure4.SimulationresultfromFlukesimulatordisplayedonmobilephone;(a)Normalsinusrhythm;
Figure 4. Simulation result from Fluke simulator displayed on mobile phone; (a) Normal sinus
(b)AtrialPrematureComplex(APC);(c)VentricularPrematureComplex(VPC);(d)Atrialfibrillation;
rhythm; (b) Atrial Premature Complex (APC); (c) Ventricular Premature Complex (VPC); (d) Atrial
and,(e)Ventricularfibrillation.
fibrillation; and, (e) Ventricular fibrillation.
TheentireevaluationofarrhythmiacanbeseeninTables2and3. ForSVEB(i.e.,APC)evaluation
The entire evaluation of arrhythmia can be seen in Tables 2 and 3. For SVEB (i.e., APC)
utilizingtheMITDBdatabase,theperformancesare79.87%,67.14%,and1.323%,respectively,forthe
evaluation utilizing the MITDB database, the performances are 79.87%, 67.14%, and 1.323%,
grossevaluationofSe,+PandFPR.Meanwhilefortheaverageevaluation,Se,+PandFPRare71.35%,
respectively, for the gross evaluation of Se, +P and FPR. Meanwhile for the average evaluation, Se,
36.9%,and2.098%,respectively.
+P and FPR are 71.35%, 36.9%, and 2.098%, respectively.
ThenextevaluationisVEB(i.e.,VPC).Forthisevaluation,utilizingastudybyHamilton[38],
The next evaluation is VEB (i.e., VPC). For this evaluation, utilizing a study by Hamilton [38],
thegrossevaluationfromAHADBforSe,+PandFPRare89.75%,96.08%,and0.371%,respectively.
the gross evaluation from AHADB for Se, +P and FPR are 89.75%, 96.08%, and 0.371%, respectively.
For average evaluation, Se, +P and FPR are 86.52%, 84.67%, and 0.458%. For MITDB database,
For average evaluation, Se, +P and FPR are 86.52%, 84.67%, and 0.458%. For MITDB database, the
thealgorithmperformancesare93.10%,95.65%and0.321%forgrossevaluationofSe,+P,andFPR,
algorithm performances are 93.10%, 95.65% and 0.321% for gross evaluation of Se, +P, and FPR,
respectively. AverageevaluationhasSe, +P,andFPRby87.27%, 73.26%, and0.336%, respectively.
respectively. Average evaluation has Se, +P, and FPR by 87.27%, 73.26%, and 0.336%, respectively.
The third database for the VEB evaluation utilizes the NSTDB database. The performance for this
Sensors2017,17,2445 7of14
ThethirddatabasefortheVEBevaluationutilizestheNSTDBdatabase. Theperformanceforthis
databaseforgrossevaluationrespectivelyfortheSe,+PandFPRare83.22%,45.79%and10.180%.
Theaverageevaluationhas58.17%,50.86%and9.032%respectivelySe,+PandFPR.
ThenextevaluationisforAFandVFrhythms. AFperformanceevaluationiscalculatedusing
MITDBdatabase. Forthegrossevaluation,theperformancesare62%,100%,92%,and92%forESe,
E+P,DSe,andD+P,respectively. TheaverageevaluationsforESe,E+P,DSe,andD+P,are70%,
100%,85%,and86%.
ThenextevaluationisVFdetection. ThisevaluationstartsusingAHADBdatabase. Forthegross
evaluation,performancesare90%,95%,28%,and97%forESe,E+P,DSe,andD+P,respectively.
AverageevaluationsforESe,E+P,DSe,andD+Pare94%,69%,33%,and70%. Theseconddatabase
used for VF evaluation is MITDB database. The gross evaluations are 100%, 75%, 69%, and 88%
forESe,E+P,DSe,andD+P,respectively. Foraverageevaluation,itis100%,33%,69%,and33%
forESe, E+P,DSe, andD+P,respectively. ThelastdatabaseutilizedforVFevaluationisCUDB.
Forthisdatabase,thegrossevaluationsare83%,90%,32%,and94%forESe,E+P,DSe,andD+P,
respectively. Meanwhile,foraverageevaluationitis84%,83%,40%,and84%,forESe,E+P,DSe,
andD+P,respectively.
Table2.Theentiresupraventricularectopicbeat(SVEB)(i.e.,APC)andventricularectopicbeat(VEB)
(i.e.,VPC)evaluationresult. (*=excluderecords2202,8205;**=excluderecords102,104,107,217;
N/A:notavailable.Se=Sensitivity,+P=PositivepredictivityandFPR=Falsepositiverate).
SVEB VEB
Database Statistics
Se +P FPR Se +P FPR
Gross N/A N/A N/A 89.75 96.08 0.371
AHADB*
Average N/A N/A N/A 86.52 84.67 0.458
Gross 79.87 67.14 1.323 93.10 95.65 0.321
MITDB**
Average 71.35 36.9 2.098 87.27 73.26 0.336
Gross N/A N/A N/A 83.22 45.79 10.180
NSTDB
Average N/A N/A N/A 58.17 50.86 9.032
Table3. Theentireatrialfibrillation(AF)andVFevaluationresult. (*=excluderecords2202,8205;
**=excluderecords102,104,107,217;N/A:notavailable.ESe=Episodesensitivity,E+P=Episode
positivepredictivity,DSe=Durationsensitivity,D+P=Durationpositivepredictivity).
AF VF
Database Statistics
ESe E+P DSe D+P ESe E+P DSe D+P
Gross N/A N/A N/A N/A 90 95 28 97
AHADB*
Average N/A N/A N/A N/A 94 69 33 70
Gross 62 100 92 92 100 75 69 88
MITDB**
Average 70 100 85 86 100 33 69 33
Gross N/A N/A N/A N/A 83 90 32 94
CUDB
Average N/A N/A N/A N/A 84 83 40 84
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Sensors 2017, 17, 2445 8 of 14

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4. Discussion
4. Discussion
This study evaluates several arrhythmias, SVEB (i.e., APC), VEB (i.e., VPC), AF, and VF,
Thisstudyevaluatesseveralarrhythmias,SVEB(i.e.,APC),VEB(i.e.,VPC),AF,andVF,based-on
based-on ANSI/AAMI EC57:2012 of totally 169 records from three PhysioNet databases with
ANSI/AAMI EC57:2012 of totally 169 records from three PhysioNet databases with applying less
applying less computationally complicated algorithms. The performances of the algorithms are
computationallycomplicatedalgorithms. Theperformancesofthealgorithmsareevaluatedbased-on
evaluated based-on the sensitivity, positive predictivity, and false positive rate. The applied
the sensitivity, positive predictivity, and false positive rate. The applied methods utilized in this
methods utilized in this study are relatively less complex; namely sample entropy, FFT, and the
studyarerelativelylesscomplex;namelysampleentropy,FFT,andtheANN.ForANN,thefeatures
ANN. For ANN, the features extracted from ECG signal are also acceptable in the feedforward run.
extractedfromECGsignalarealsoacceptableinthefeedforwardrun. Thisconditionhaspurposedto
This condition has purposed to minimize the computational time, while performing testing in the
minimizethecomputationaltime,whileperformingtestinginthereal-timeapplication.
real-time application.
In order to study the measurement evaluation of previous studies conducted based-on
In order to study the measurement evaluation of previous studies conducted based-on
ANSI/AAMIEC57,theresultsarecomparedtoourresultsasshownonTable4forSVEBandVEB
ANSI/AAMI EC57, the results are compared to our results as shown on Table 4 for SVEB and VEB
results. ForSVEBandVEB,astudyconductedbyDeChazaletal.[44]isinvestigated. Thisstudy
results. For SVEB and VEB, a study conducted by De Chazal et al. [44] is investigated. This study
showedthatSVEBevaluationproducedgrossevaluationofSe,+PandFPRas75.9%,38.5%,and4.7%,
showed that SVEB evaluation produced gross evaluation of Se, +P and FPR as 75.9%, 38.5%, and
4.7%, respectively. For comparison purposes, we found that our study has better performances with
Sensors2017,17,2445 9of14
respectively. Forcomparisonpurposes,wefoundthatourstudyhasbetterperformanceswithrespect
togrossevaluationofSe,+PandFPR,whichare79.87%,67.14%,and1.323%,respectively. ForVEB
classification,DeChazaletal.[44]hasgrossevaluationofSe,+PandFPRare77.7%,81.9%,and1.2%.
Meanwhile,withutilizingastudybyHamilton[40],ourresultsalsoshowedbetterachievementby
producing93.1%,95.65%,and0.321%,respectively,forgrossevaluationofSe,+PandFPR.
Table4.TheSVEB(i.e.,APC)andVEB(i.e.,VPC)resultcomparison.
Sensitivity(%) PositivePredictivity(%) FalsePositiveRate(%)
ThisStudy DeChazaletal.[44] ThisStudy DeChazaletal.[44] ThisStudy DeChazaletal.[44]
SVEB 79.87 75.9 67.14 38.5 1.323 4.7
VEB 93.1 77.7 95.65 81.9 0.321 1.2
For AF study, results are compared with a previous study conducted by Young et al. [45].
This study performed hidden Markov model (HMM) evaluation using ANSI/AAMI:EC57 for
evaluation. TwelveMIT-BIHArrhythmiadatabaserecordswereutilizedfortraining. Furthermore,
for testing data, MIT-BIH AF database was used. In order to perform a comparison to this study,
only training results from Young et al. study are investigated. From the results of Young et al.
study,asensitivityevaluationprovidesabetterresultof97.7%ascomparedtothisstudyevaluating
44MIT-BIHArrhythmiadatabaserecordsforESethathas62%,andDSethathas92%forgrossstatistics
evaluations. However,thisstudyproducesbetterresultsforbothE+Pthathas100%,andD+Pthat
has92%forgrossstatisticsevaluationsincomparisontotheirstudy,whichresultedin86.77%forthe
positivepredictivity.
ForaVFcomparisonstudy,studiesbyParketal.[46]andMoraesetal.[47]areutilized. Astudy
byParketal. evaluatedAHADBandMIT-BIHarrhythmiadatabasesbyapplyingdecisionrule-based
algorithmandutilizingANSI/AAMI:EC57. Thisstudyhasevaluatedthedurationsensitivityand
durationpositivepredictivity. ForAHADBevaluations,astudybyParketal.,utilized11recordsfrom
AHADB,have98.1%and99.1%,respectively,fortheDSeandD+Presults. Meanwhile,ourstudy,
evaluating78recordsfromAHADB,forDSeandD+Pproduces28%and97%,respectivelyforgross
statisticsevaluations.Furthermore,forMIT-BIHarrhythmiadatabaseevaluation,astudybyParketal.,
evaluatedonlyrecord207,has88.5%and86.3%forDSeandD+P,respectively. Hence,thisstudy,
utilizing44records,hasachieved69%and88%forDSeandD+P,respectively.
Another study is performed for the purpose of VF evaluation comparison. Moraes et al. [47]
conductedastudybycombiningtwoalgorithms,VFfilterleakageandcomplexitymeasurealgorithms.
ThisstudyhasalsoutilizedANSI/AAMI:EC57fortheevaluationofCUDB.Thecombinedalgorithmby
Moraesetal. studyprovidedsensitivityandpositivepredictivityevaluations,byutilizing30records.
For comparison study purposes, our results, utilizing 35 records, have 83% for ESe and 32% for
DSegrossstatisticsevaluations. Meanwhile,astudybyMoraesetal. has70.32%forthesensitivity.
Inaddition,theresultsofthisstudyhave90%forE+Pand94%forD+Pgrossstatisticsevaluations.
However, study by Moraes et al. has 94.66% positive predictivity. The overall comparison of the
PhysioNet-baseddatabasefortheAFandVFcanbeseenbyTable5.
TheevaluationofAFevaluationinthewearableECGdeviceiscomparedtostudybyLinetal.[8].
This study applied the expert system algorithm. A three-lead ECG device with 512 Hz sampling
frequencyand12-bitresolutionwasutilizedfor10normaland20AFpatients. Fortheevaluation,
the12-leadECGsystemresultwasinvestigatedbythecardiologists. Thesensitivityandthepositive
predictive performance are 94.56% and 99.39%, respectively. For the comparison, Lin et al. study
performedbetteraccuracythanourstudy. However,ourstudyevaluatestheepisodeandduration
separatelyaccordingtoANSI/AAMI:EC57forthesensitivityandpositivepredictivity.
For computational time, a study by Chakroborty et al. is utilized for the comparison [48].
Thisstudyproposedasolutionforthearrhythmiaclassifications.Theclassifiedarrhythmiasarenormal,
left bundle branch block (LBBB), right bundle branch block (RBBB), PVC, and PAC. The MIT-BIH
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Arrhythmia database was utilized for the evaluation. This study provided the overall evaluation
time is 6875.3 s. For this study in computational time evaluation, the personal computer (PC),
and smartphone-based computational time are evaluated. The PC specification is MacBook Air,
Intel Core i5, and 1.6 GHz. Meanwhile, the smartphone is iPhone 5S, A7 chip, 64-bit architecture,
and1.3GHzfrequency. TheresultsshowthatthecomputationaltimeforthePC-basedcalculationis
about174.802s. Luckily,smartphone-basedcalculationproduces1840.791s. TheevaluationtimeinPC
andsmartphoneforMIT-BIHArrhythmiadatabasecanbeseeninTable6. Onaverage,thePC-based
computationalis3.591sandthesmartphone-basedtakes41.836s. Meanwhile,ourproposedstudyhas
beenshownlesscomputationaltime,asshowninTable7. However,thiscomparisonmaynotbefully
acceptableduetothecomparisonofLBBBandRBBBdetectionsversuswithAFandVFevaluations.
There are several limitations of this study. The first comes from the sliding window for
detectingAF.InthisstudyevaluationusingsampleentropyneedsahugenumberoftheR-Rinterval.
Thisconditionmakesdelayfortheevaluation. However,accordingtoLoganetal.,the5-minsliding
windowisanacceptablewaitforAFdetectioncases[49].
The second one, ideally, the evaluation of AF and VF should be performed at the same time.
However,AFevaluationisperformedbased-onRRintervalandVFevaluation,whichisbased-onraw
ECGsignal,theseconditionswillaffectoneanother. ForAF,whenevaluationfollowsVFdetection
usingtherawECGsegment,itwillbeinanRRshortageconditionfortheevaluation. Meanwhile,
forVF,whentheevaluationfollowstheAFdetection(i.e.,theR-Rinterval-basedcalculation),ithighly
likelymixessomerhythmsinsidethecalculationwindow.
Thenextlimitationisthealgorithmsequence. DuetoourstudyplacingVFdetectionasthelast
evaluation, it may appearthat VF waveand signal that isclose to itswave are similar tothe QRS
complexclassifiedasVPCclass,asshowninFigure6. Thisdisadvantageishighlylikelytobeoneof
thefactorsnegativelyaffectingVFdetection.
ForSVEB(i.e.,APC)detection,accordingtoANSI/AAMIEC57,theevaluationshouldcoverall
datarecords. Thisconditionwillmakearequirementtoevaluatenotonlytestingandvalidationdata,
butalsotrainingdataoftheANN,whichislearntbythemodelinthetraining.
The device also has several limitations. For this system, the microcontroller unit speed is up
to 25 MHz and the Bluetooth module will only support the data transferring maximum 2 Mbps
datarates. Inthisstudy,utilizingsingleleadevaluation,theseMCUandBLEstillworkverywell.
However,theseconditionswillmakeourdevicehighlylikelytohaveaproblemforthemulti-lead
ECGsignalevaluation.
For the electrode, this study utilizes a wet-based electrode. This electrode may have some
disadvantagesforthelong-termuser. Thedry-up[50]andsweating[51]mayaffectthequalityof
thesignal. Infurther,theutilizationofthedryelectrodewillgeneratesolutionsforthewetelectrode
limitationsinbiopotential-basedevaluation[52–55]. Thedryelectrodewillbeoneofthefutureworks
forourstudy.
Table5.TheAFandVFresultcomparison.(N/A=notavailable).
Evaluation
Number
Arrhythmia Studies Database GrossStatistics
ofData Se +P
ESe E+P DSe D+P
Atrial Proposedstudy 44 62 100 92 92 N/A N/A
MITDB
Fibrillation Youngetal.[45] 12 N/A N/A N/A N/A 97.7 86.77
Proposedstudy 78 90 95 28 97 N/A N/A
AHADB
Parketal.[46] 11 N/A N/A 98.1 99.1 N/A N/A
Ventricular Proposedstudy 44 100 75 69 88 N/A N/A
Fibrillation Parketal.[46] MITDB 1 N/A N/A 88.5 86.3 N/A N/A
Proposedstudy 35 83 90 32 94 N/A N/A
CUDB
Moraesetal.[47] 30 N/A N/A N/A N/A 70.32 64.66