Check your biosignals here: a new dataset for off-person ECG biometrics

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j ou rn a l h om epa ge :w w w . i n t l . e l s e v i e r h e a l th . c o m / j o u r n a l s / c m p b

Check

Your

Biosignals

Here:

A

new

dataset

for

off-the-person

ECG

biometrics

Hugo

Plácido

da

Silva

a,∗

_,

_André

_Lourenc¸o

a,b

_,

_Ana

_Fred

a

_,

_Nuno

_Raposo

c

_,

Marta

Aires-de-Sousa

c

a_Instituto_de_{Telecomunicac¸ões,}_Instituto_Superior_Técnico,_1049-001_Lisboa,_Portugal b_Instituto_Superior_de_Engenharia_de_Lisboa,_1959-007_Lisboa,_Portugal

c_Escola_Superior_de_Saúde,_Cruz_Vermelha_Portuguesa,_1300-125_Lisboa,_Portugal

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received12March2013 Receivedinrevisedform 26November2013 Accepted28November2013 Keywords: Biometrics Electrocardiography Off-the-person Dataset

a

b

s

t

r

a

c

t

TheCheckYourBiosignalsHereinitiative(CYBHi)wasdevelopedasawayofcreatinga datasetandconsistentlyrepeatableacquisitionframework,tofurtherextendresearchin electrocardiographic(ECG)biometrics.Inparticular,ourworktargetsthenoveltrendtowards off-the-persondataacquisition,whichopensabroadnewsetofchallengesand opportuni-tiesbothforresearchandindustry.WhiledatasetswithECGsignalscollectedusingmedical gradeequipmentatthechestcanbeeasilyfound,foroff-the-personECGdatathesolution isgenerallyforeachteamtocollecttheirowncorpusatconsiderableexpenseofresources. Inthispaperwedescribethecontext,experimentalconsiderations,methods,and prelim-inaryﬁndingsoftwopublicdatasetscreatedbyourteam,oneforshort-termandanother forlong-termassessment,withECGdatacollectedatthehandpalmsandﬁngers.

1. Introduction

Aresearchﬁeldthathasseenarecenttrendtowardstheuseof biosignalsisbiometrics,whichfocusesonidentityrecognition basedon physiological orbehavioral properties ofan indi-vidual[1].Electrocardiographic(ECG)dataisoneofthenovel biometrictraitswhereagrowinginterestisevidentwithinthe referenceliterature[2–6].Aparticularlyimportantaspect,that istransversaltoalltheworkdonetodate,istheaccesstolarge datasetstoevaluatetherobustnessofthedevisedmethods acrossresearchteams.Overtheyears,severalinitiativeshave greatlycontributedtomitigatethisproblem,mostlyby pro-vidingrepositoriesfordatacollectedinclinicalandlaboratory settings[7].

∗ _{Corresponding}_author._Tel.:₊₃₅₁_919315567.

E-mailaddresses:hsilva@lx.it.pt(H.P.daSilva),alourenco@deetc.isel.ipl.pt(A.Lourenc¸o),afred@lx.it.pt(A.Fred),nraposo@esscvp.eu

(N.Raposo),msousa@esscvp.eu(M.Aires-de-Sousa).

Our researchgroup recentlypivotedtowards adifferent approachentitled“off-the-person”[8–11].Arecurrentproblem facednotonlybyus,butalsobyotherresearchers through-out the world,isthe accesstolargedatasets, inparticular collected in arepeatable manner,and that canbe publicly accessible.Thisconcernisespeciallyimportantwhendealing withECGacquired inanon-traditionalfashion, with mini-mallyconstrainedorunconstrainedscenarios,andthatcan accountforchangesintheenvironmentalconditions,aging, andotherfactorssurroundingthesubjects.

The Check Your Biosignals Here initiative (CYBHi) was motivatedbythelackofdatasetsofECGsignalscollectedat thehandpalmsandﬁngers(i.e.off-the-person).Whatis gen-erallyfoundinthestate-of-the-artisthateachteamcollects

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Table1–SummaryofdatasetsfoundintheECGbiometricsliterature.

Name MIT-BIH[7] AHA[7] PTB[7] DRIVEDB[7] CYBHi Chan[3] Zhao[6] Odinaka[4]

Numberofsubjects 48 155 290 17 125+ 50 22 265

Samplingrate 360Hz 250Hz 10kHz 496Hz 1kHz 1kHz 200Hz 1kHz

Resolution 11-bit 12-bit 16-bit 14-bit 12-bit 12-bit n.a. n.a.

Channels 2 2 14 1 2 1 1 1

Contactpoints n.a. n.a. n.a. 3 2 2 3 n.a.

Electrodes Gel Gel Gel Gel DryAg/AgCl+electrolycras DryAg/AgCl Gel Gel

Placement Chest Chest Chest Chest Handpalms+ﬁngers Fingers Fingers Chest

Publiclyavailable Yes Yes Yes Yes Yes No No No

itsowncorpora,consistingofonlyafewdozensof individ-ualsorless,atgreatﬁnancialandtimeexpense,whichisthen onlyusedinthecontextoftheirownresearch,andoftenlacks severalrelevantdetailsregardingacquisitionandsubject con-ditions.Assuch,wedevisedadataacquisitionframeworkand experimentalsetup,forlargescaledatacollectionfromalarge groupofsubjectsthroughaneasilyrepeatableand efﬁcient procedure.

Therest ofthe documentis organized as follows: Sec-tion2outlinesthecurrentlandscapeintermsofECGdatasets; Section3describestheexperimentalsetup,highlightingthe maintechnicalchallengesandoptionstoovercomethem; Sec-tions4and5describetheadoptedmethodsandprocedures fordataacquisition;Section6presentstheﬁndingsresulting fromresearchdonetodatewithCYBHi;Section7describesthe resourcesmadeavailableasaresultofourwork;andﬁnally, Sections8and 9provide ashort discussionand insighton futureworkresultingfromtheCYBHi.

2. Background

Recognizingtheneedandusefulnessofcentralizeddatasets thatcanbeusedasacommonreferenceforresearchers world-wide,severalinitiativeshavebeencontributingwithresources tomitigatethis.Physionet [7] iscurrentlyoneofthe main forumsfordisseminationandexchangeofbiomedicalsignals ingeneral.Mostpubliclyavailabledatasetsarecurrently cen-tralizedonthePhysioBankrepository,andcontainmultiple parameters from healthy and pathological conditions (car-diorespiratory,neuralandothers).However,inwhatconcerns ECG datasets, public resources found to dateonly contain signalscollectedatthechestwithclinicalgradeequipment.

Table1 highlightsthe speciﬁcations ofdatasets commonly foundinECGbiometricsliterature.

Electrocardiographic signal acquisition methods can be classiﬁedaccording totheirintrusivenessasfollows[8]:(a)

In-the-person:referringtoimplantabledevicessuchas artiﬁ-cialcardiacpacemakersandimplantableloop recorders;(b)

On-the-person:referringtodevicesthatneedtobeattachedto thebodyofthesubject,generallyrequiringconductivepaste orgel;and(c)Off-the-person:referringtodevicesthatare inte-gratedinobjectsorsurfaceswithwhichthesubjectsinteract with(e.g.acomputerkeyboard),anddonotrequireanyspecial preparationofthesubject.

Researchsofarhasshownbroadevidenceregardingthe applicability of ECG signals collected in an on-the-person approachforbiometricpurposes(see[2,4,5] andreferences therein).Nonetheless,thisapproachishighlyintrusiveforthe

subjects,thuslimitingthepotentialindustrialapplicationsof ECG-basedbiometrics.Morerecently,researchershavebeen pivoting towards the off-the-person approaches [3,6,10–13], inanefforttoovercometheintrusivenessproblemswidely criticizedinECGbiometrics.Recentworkfromourteamhas provided experimental evidence that the signals obtained through the off-the-person approach using dry electrodes, presentvirtuallythesamemorphologyasthoseobtainedat theLeadIderivationofastandard12-leadmedical-gradeECG system[8,14].

Still, tofurther progresson the study ofthis particular research topic,no public datasets currentlyexist (Table1). Furthermore,inthe non-publicoff-the-persondatasets, the amountofinformationprovidedregardingthematerialsand methods oftenlacksseveral relevantdetails.In this paper, weseektocontributetoﬁllthisgap,byprovidingadetailed description ofour experimentalsetupfordataacquisition, withthepurposeofcreatingalargeandextendablecorpora ofdata,targetingthespeciﬁcclassofoff-the-personand min-imallyconstrainedsignalacquisitionscenarios[8].Ourgoal is to set the ground for a scalable family of datasets that enables researchers to benchmark their algorithms taking into accountfactorssuchasthe permanenceofthe biosig-nalsthroughouttime,emotionalarousal,amongothers.Initial acquisitionsessionstargetedthecollectionofECGsignalsin highly non-intrusive scenarios, and our dataset is publicly availableforthecommunityatlarge.

3. Design

considerations

3.1. Overview

Giventhemotivationofourstudytowardstheoff-the-person approach,weproposedasetupforECGdataacquisitionatthe hand palmswithdryAg/AgCl electrodesandattheﬁngers withElectrolycras,toassessthebiometricpotentialofsignals collectedattheseanatomicregionsandcomparethe perfor-manceofbothmaterials.Moreover,ourprotocolincludesboth neutraltasksandemotionalelicitationtasks[15,16].Thelater wereintroducedasawayofinducingintra-subject variabil-ity,whichisanimportantaspecttotakeintoaccountwhen benchmarkingECG-basedbiometricalgorithms.Forthis pur-pose, electrodermalactivity (EDA)datawas simultaneously acquiredasawayofprovidinggroundtruthinformationabout the arousalstate ofthe subject, andwhich canbeusedin correlationwiththeECGdata.

Theinclusionofemotionelicitingstimuliwasmotivated by the factthat authors in the ﬁeld ofECG-biometrics are

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Fig.1–Overallworkbenchsettingfordataacquisition.

Fig.2–Electrodermalactivitysensorandelectrodes.

startingtoexplore the relationbetween emotionalarousal andchangestotheECGheartbeatwaveform[17,18].In[17], visual stimuli were used to induce positive and negative emotionsforvalenceandpassivearousalperformance eval-uation, and a video game was used to attempt to induce active mental stress.In [18] the ECG was acquired during human–computer interaction on different cognitive activi-ties.Bothstudiesreporttheexistenceofslightmorphological changestotheECGheartbeatwaveformassociatedwiththe presenceofdifferentemotional/affectivestates.

Fig.1,showstheoverallworkbenchsetting,comprisedof adevicetodisplaymultimediacontenttothesubject,theset ofsensorsusedintheexperiments,andthebiosignal acquisi-tionequipment,togetherwiththebasestationsforreal-time datarecording.Thearrangementwassuchthatthesubject satononesideofthetable,andtheexpertsontheotherside. Allparticipantsweresubmittedtoaninitialbrieﬁngwiththe supportofaninformedconsentdocument(Fig.9).Thisform wasreadtothesubjectbythecoordinatoroftheexperiments, andclearlystatedthematerialsinvolvedintheexperiments,

Fig.3–Electrocardiographysensorsandelectrodes arrangement;onthetopwecanseetheelectrolycrastrips, andonthebottomthedryAg/AgClelectrodes.

thepurposeofourresearch,andthedifferentstepsthatwould beperformed.

To acknowledge the participation as a volunteer and grant the anonymous use of the collected data, subjects were requiredtosign thedocument.Participantswerealso inquiredaboutthepossibilityofparticipatinginfuturedata acquisition sessions, and primarycontact informationwas collectedforthispurpose.1_Demographic_information_was_also

requestedforstatisticsandsamplecharacterizationpurposes,

1 _It_is_important_to_further_reinforce_that_contact_information_is

storedseparatelyfromthedataandthatthedatabaseis anonymized.

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Table2–Videosequencescharacterization.

Seq. Type Timeframe Content

A1 Low 0–55s SonyBraviapaintad

Arousal 55–60s Monkeyfallsoftree

A2 High 0–55s SonyBraviabouncyballsad

Arousal 55–60s FinalpartofRECmovietrailer

andaspaceforadditionalnotesorcommentsregardingthe

experiments wasalsoadded.Itisimportanttohighlightthat

allthedatapublicly availableintheCYBHidataset is

com-pletelyanonymizedtopreservetheprivacyoftheusers.

3.2. Computertask

Inthedesignoftheexperiment,weintroducedasetofﬁlm

clipsthatwouldserveasanactivityfortheusertoperform

whilethesignalacquisitionprocesswastakingplace,butthat

wouldalsoelicitnon-speciﬁcaffectiveresponses.Inorderto

potentiate noticeablereactions inthesubjects we

manipu-lated the movies to introducea stimuli.Two MPEG4 video

sequenceswereprepared,witha1280×720pixelresolution

and total duration of 1min; a background soundtrackwas

alsomaintainedforgreaterimmersionandstimulatory

poten-tial.Inthesesequences,thelast5swereadjustedinorderto

presentatriggeringstimuli.

Toallow synchronization with the biosignal acquisition

system,andalsotomarkthebeginningofthevideoandthe

stimulipresentation,awhitesquarewith120×120pixelswas

insertedonthetopleftcornerofthescreen,whichappeared

for2sinthebeginningofthevideoandinthemomentwhere

the triggering stimuli is presented. An Apple iPad 2tablet

devicewasusedtodisplayandreproducethemultimedia

con-tent; the device wasplaced ona verticalstand infront of

theperson(Fig.12(a)),andasetofheadphoneswereused,to

providegreaterfocusonthereproducedcontentand maxi-mizetheimpactofthepreparedstimuli(Fig.12(b)).

Table2describesthecontentusedtoproduceeachstimuli. SequenceA1wasdesignedasamoreamusingvideo,to stim-ulatelowarousal;it started withanexcerptofanad with afairlyintenseaudiosoundtrackandsurprisingmoments,2

whichswitchedtoashortfunnyvideoneartheend.3_Sequence

A2wasdesignedasamoreintensevideotostimulatehigh arousal;itstartedwithapeacefulsequenceextractedfroman ad,4_and_switched_to_an_excerpt_of_a_horror_movie_trailer.5_Fig.₇

showssampleframesfrombothaffectiveelicitationvideos.

3.3. Biosignalsensors

Electrocardiographic (ECG) data acquisition was performed withacustom,twoleaddifferentialsensordesignwith vir-tual ground, proposed in [19]; Table 3 outlines the main speciﬁcations.TwoECGsensorswereusedinourexperimental setup,oneforsignalacquisitionatthehandpalmswithdry

2 _{http://www.youtube.com/watch?v=wwO-wo892pI}_. 3 _{http://www.youtube.com/watch?v=zea6SCzjtfw}_. 4 _{http://www.youtube.com/watch?v=0}_bx8bnCoiU_. 5 _{http://www.youtube.com/watch?v=YGJ}_jPKOj1c_.

Table3–SpeciﬁcationsoftheECGandEDAsensors.

ECG EDA

Filtering 1–30Hzbandpass 3Hzlowpass

InputImpedance >1M >1T

CMRR 110dB 110dB

Gain 1000 –

Ag/AgCl electrodes,and anotherforsignalacquisitionwith

Electrolycrasattheindexandmiddle ﬁngers.Forimproved

comfortandgreaterefﬁciency,theECGsensorswereﬁttedto

aleatherbase,withtheintendedhandplacementsignaledin

anunequivocalway.

Fig.3depictsthedevisedsensorandelectrodes arrange-ment. One of the ECG sensors was connected to the dry Ag/AgClelectrodesthatwereplacedatthebaseofthehand palms,nearthethenareminence;theotherECGsensorwas connectedtotheElectrolycrastripsplacedalongtheindexand middleﬁngers.Thetransferfunctionforthissensorisgiven byEq.(1);VO istheoutputvoltageofthesensorandVECGis

theECGsignalvoltage,bothinVolt.Eq.(2)providesan equiv-alentrepresentation,whichusesthedigitaloutputcodesDO

oftheADCdirectly.Inoursetupweusedasystemwith12-bit resolution(n=12),Vcc=5VandVss=0V.

VECG= ₁₀₀₀VO (1)

VECG= DO∗Vcc

2n_×103 (2)

Aspreviouslymentioned,simultaneousacquisitionofEDA datawasalsoperformedtoassessthearousallevelofeach par-ticipant.WerecurredtothecommerciallyavailableedaPLUX,6

which has two independentsensor leads, allowing a high level ofﬂexibility in the sensorplacement. Pre-gelled and self-adhesiveAg/AgClelectrodes7_were_used_as_interface_with

theskinforimprovedconductivity.Table3outlinesthemain speciﬁcationofthesensor,andFig.2depictsthesensorand electrodes.

ThetransferfunctionforthissensorisgivenbyEq.3;VO

istheoutputvoltageofthesensorinVolt,andGSistheskin

conductanceinS(microSiemens).Eq.4providesan equiv-alentrepresentation,whichusesthedigitaloutputcodesDO

oftheADCdirectly.Inoursetupweusedasystemwith12-bit resolution(n=12),Vcc=5VandVss=0V.

GS=₀V_.2O (3)

GS=D₂O_n_××₀V_.2cc (4)

3.4. Dataacquisition

RawbiosignalswereacquiredwithabioPLUXresearch,8

Blue-tooth wireless biosignal acquisition unit; this device was

6 _{http://www.biosignalsplux.com}_. 7 _{http://www.spesmedica.it}_. 8 _{http://www.biosignalsplux.com}_.

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Table4–SpeciﬁcationsofthebioPLUXresearch.

Connectivity ClassIIBluetoothconnectivity

Range upto10m

Resolution upto12bit

Samplingrate upto1000Hz

Weight 74g

Size 84mm×53mm×18mm

Battery Li-On;7.4V;800mAh

usedina12-bitresolution,1kHzsamplingfrequency

conﬁg-uration.ToguaranteeelectricalisolationbetweenbothECG

sensorsusedintheexperiments,twoindependentbiosignal

acquisitionunitswereused.Table4describesthemain

spec-iﬁcationofthebiosignalacquisitionunit.

Fig.8depictsthehardwareconﬁgurationusedinour exper-imentsforbiosignalacquisition.Aspreviouslydescribed,an iPad was used for to reproduce the multimedia contents presentedduring the computer task/acquisition; two Light DependentResistor(LDR)sensorsweretapedwithblacktape (fordiscreetnesspurposes),tothecorneroftheiPadscreen abovetheareawherethevideo-embeddedwhitesquarewould appear.TheECGsensorarrangementwasplacedinfrontofthe iPad,insuchwaythatthepersoncouldcomfortablyrelaxthe handswhileperformingthecomputertask,thatis,watchthe videoclips.Electrodermalactivitysensorswereplacednear theECGsensorarrangementtoenablefastapplicationtothe person.Allthesensorswereconnectedtoeachofthe biosig-nalacquisitionunitsthatwereplacedclosetotheremaining apparatus.Table5presentsthechannelmappingforeachunit.

3.5. Timesynchronization

Time synchronization between both biosignal acquisition unitswasperformedopticallytoensurefullelectrical decou-pling,usingelementsfromasyncPLUXsynchronizationkit.To oneoftheunits,weconnectedaswitch,whichsimultaneously activatedalightemittingdiode(LED),andtriggeredaTTL sig-naltothedigitalinputportofthedevice;totheotherunit, weconnectedaLDR,whichwasdirectlyattachedtotheLED. Withthissetup,whenevertheswitchwaspressed,acommon signalwasrecordedbybothdevices,enablingtimealignment inthepost-processing(Fig.4).

A second synchronization mechanism was also imple-mented using LDR sensors, to guarantee synchronization betweenthebiosignalacquisitionunitsandthemultimedia contentdisplayed. For this purpose,two LDR sensorswere appliedtotheiPadscreenandconnectedtoeachofthe biosig-nal acquisition units. In a pre-test stage, the videos were playedto capturea “videoprint”based onthe screen light intensityvariations,whichcanbeusedtorelatethebiosignals activitywiththestimulibeingpresented.

Furthermore, this mechanism allowed us to introduce redundancyinthesynchronizationprocess,asthe informa-tionextractedfromthevideoprintsalonecanbeusedfortime alignmentofthesignalscollectedbyeachindependentunit. SincetheoperatingprincipleoftheLDRsensorsisalso opti-cal,electricaldecouplingbetweenbothindependentbiosignal acquisitionunitsisonceagainguaranteed.

Fig.4–Examplesofthesignalsextractedfromourinitial experiments.Allsignalsarenormalizedforimproved legibility.OnthetopchartweshowtheLED+TTLSwitch synchronizationsignals,whiletheECGsignalscollected withbothelectrodematerialsareshowninthebottom chart.

4. Short-term

dataset

4.1. Participants

Theexperimentalsetupwasplacedonanunrestrained set-ting,inthelobbyofthebuildingwhereoneofourresearch teams is located: Torre Norte of the Alameda Campus of InstitutoSuperiorTécnico.Togenerateawarenessforthe ini-tiativeandincreasecompliance,asetofpromotionalbanners wascreatedandplacedclosetotheworkbench;avolunteer counter,wasregularlyupdatedtofurthermotivatepotential candidates. Fig.11 showsthedeviseddesigns,seenalsoin theirintendeduseinFig.1(a).

Sessionstookplaceovertwofulldays,andintheenddata hadbeencollectedforanoveralltotalof65participants,the majorityofwhichwereengineeringstudentsandresearchers. The demographics shown 49 males and 16 females, with anaverageageof31.1±9.46years.Noneoftheparticipants reportedanyhealthproblems,reasonforwhichweconsider thecollecteddatatoberepresentativeofthehealthy popula-tion.

Asarewardforparticipatingintheexperiments,agiftcard createdfromthecollectedbiosignalswasprepared(Fig.10). Withtheindividualheartbeatwaveforms,anorangecolormap imagewascreated,whereeachlinecorrespondstoan indi-vidualheartbeatwaveform,andeachcolumncorrespondsto the amplitude ofeach sample ofthe heartbeat wave data. WiththeindividualEDAevents,abluecolormapimagewas createdusingthesameprinciple,butinthiscaserotatedof −90◦_.

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Table5–Channelmappingsandsensorreferences.

Ch. Sensor Placement

00:07:80:42:0F:8BbioPLUXresearch

1 ECG-3-82 Handpalms(withAg/Clelectrodeandnogel)

2 EDA-1-25 Lefthand

3 LDR-1-4 iPadscreen

4 LDR-1-7 LED

00:07:80:42:0F:85bioPLUXresearch

1 ECG-3-83 Indexandmiddleﬁngers(withelectrolycra)

5 LDR-1-1 iPadscreen

i/o SYNC Syncadapter+LED(indirectcontactwithLDR-1-7)

4.2. Experimentalprocedure

Thewholeexperimentwasdesignedtohaveanaveragetotal

completiontimeofapproximately5min,scattered

through-out threestages:(a) Informedconsent(CI);(b)Lowarousal

video(A1);and(c)Higharousalvideo(A2).Onceparticipants

willinglyshowedinterestinbecomingpartoftheexperiments,

theﬁrststagewouldtakeplace,andconsistedonhavingthe

coordinator oftheexperiment going throughthe informed

consent(Fig.9),explainingtheprocedureindetail,goalsofthe

study,andrelatedbackgroundwork.Duringthisphase,only ECGsignalsatthehandpalmsandﬁngerswererecorded,and noemotionelicitingstimuliwerepresented.

Forthe secondphase,subjectswere fittedwiththeEDA sensorontheleft hand,inwhichoneterminalofthe sen-sorwas placedon themiddle phalanx oftheindex finger, andtheotheronthemiddlephalanxoftheringfinger.The headphoneswereplacedovertheears,andthelowarousal videowasstartedontheiPadbythecoordinatorofthe exper-iment.Forthethirdphase,theheadphoneswerekeptonthe subject,andthevideoontheiPadswitchedtothehigharousal videosequence.Duringboththesephases,ECGsignalsatthe palms,andEDAsignalswererecorded.

Ateachstage,recordingsfromeachindividualdevicewere storedinaseparatefileformoreefficientpost-processing,and labeledwiththedate,identificationofthesystem,andacode assignedtothesubject.Inthebeginningandintheendofeach recordingtheswitchwouldbetriggeredinordertoproducethe synchronizationsignal,necessarytofindthecommontime baseaspreviouslydescribed(Fig.4).

5. Long-term

dataset

5.1. Participants

Thelong-termdatasetwascollectedoveraperiodofseveral days and different settings, reason for which a slimmed-downversionoftheproposedacquisitionsetupwasadopted. Weusedasinglebiosignalacquisitionsystemwithjustone sensortomeasureECGﬁttedwiththedryAg/AgClelectrodes. Theexperimentalsetupwaspreparedatthe cardiopneumol-ogylaboratorylocatedattheEscolaSuperiordeSaúdedaCruz VermelhaPortuguesa,thatwaspreparedtoreceivethe partic-ipants.

Therecruitmentofvolunteerswasperformedby advertis-ing thedaysinwhich dataacquisition sessionswere held, andbyprovidinganoverviewoftheactionduringclasses.A

totalof63subjects(nursingandhealthtechnologiesstudents), wereenrolledintheexperimentandparticipatedinthetwo acquisitionsessions.Thedemographicsshowed14malesand 49 females,withanaverageageof20.68±2.83years.None oftheparticipantsreportedanyhealthproblems,reasonfor whichweconsiderthecollecteddatatoberepresentativeof thenormalpopulation.

5.2. Experimentalprocedure

Twodataacquisitionmomentsseparatedbya3-month time-frametookplace,andenableddatarecollectionfromtheset ofvolunteersenrolledintheexperiment,withthepurposeof studyingthechangesintheECGmorphologyovertime.Inthe ﬁrstmoment,thecoordinatoroftheexperimentpresentedthe informedconsenttoprospectiveparticipants(Fig.9), explain-ing the procedure indetail,goalsofthe study,and related backgroundwork.Onceparticipantswillinglyshowed inter-estinbecomingpartoftheexperiments,theywererequired tosignthedocumentandwereenrolledinthedatacollection program.

In both moments only ECG signals at the fingers were recorded,andineachofthesessionsthesubjectswereasked tositfor2mininarestingposition,withtwofingers,onefrom theleftandanotherfromtherighthand,placedineachofthe dryAg/AgClelectrodes.Therecordingswerestoredin individ-ualfilesformoreefficientpost-processing,andlabeledwith the date,identification ofthesystem, and acode assigned to thesubject. The factthat it hastwo sessions separated byseveralmonthsapartmakesthisdatasetparticularly use-fulforbenchmarkingtheperformanceofidentityrecognition algorithms taking into account potential variations in the heartbeatwaveformovertime.

6. Status

report

6.1. Outline

Ourexperimentstodatehavealreadyenabledseveral interest-ingﬁndingsusingthecollecteddata.First,asshowninFig.4

(bottom), the optical synchronizationmethod enablestime alignment withinthemillisecond,allowingthe signals col-lectedbyeachindependentunittobecomparedinthesame timebase.Also,aswecanseefromtheexamplepresented inFig.5,boththeECGsignalscollectedwithdryAg/AgCland Electrolycrasatthehandpalmsandattheﬁngersretainahigh levelofdetailwithrespecttotheheartbeatwaveform.

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Fig.5–ECGsignalsatthehandpalms(dryAg/AgCl)and ﬁngers(Electrolycras).

Fig.6–ExampleofaﬁlteredandsegmentedECGsignal collectedatthehandpalmswithdryAg/AgCl.Theblack linedepictstheaverageofallwaveforms,whilethedashed linesdepictthestandarddeviation;thegraylinesshowan overlayofthesegmentedindividualheartbeatwaveforms foroneoftheacquisitionsessions.

Inparticular,not onlythe so-importantQRScomplex is clearlynoticeable,butalsothePandTwavescanbefound. Moreover,wecanseethatthereisahighmorphological sim-ilaritybetweenbothsignals,leadingustobelievethatboth theelectrolycrasandthedryAg/AgClelectrodescanbeused asinterfacewiththeskin,givinghigherﬂexibilityand con-venienceto the biometric system designer. Thisis further reinforcedbytheanalysisperformedin[8,14].

Fig.7–Sampleframesfromthevideoandstimuli sequences.

Fig.8–Fullexperimentalsetup.

Anadditionalexampleofthecollectedsignalsispresented inFig.6,wherewecanseeanexampleofaﬁlteredand seg-mentedrecordingcollectedatthehandpalmswiththedry Ag/AgClelectrodes.

Still,signalscollectedatthehandsandfingers,especially using dry materials as electrodes, have a lower signal-to-noiseratio,whichraisesseveralchallengesnamelyinterms offiltering,segmentation,andoutlierdetection.Afirstsetof guidelinesregardingthepre-processingofthesesignalscan befoundintheworkbyLourençoetal.[9,10];morerecently, toenhancetheECGsignalqualityandincreasethe signal-to-noiseratiointheECGbiometricscontext,westartedtoadopt a300orderbandpassFiniteImpulseResponse(FIR)filter,with aHammingwindow,andcutofffrequenciesof5Hzto20Hz. Thesespecificationstake intoaccounttheECGinformation bandwidth and empiricalconsiderations obtainedfrom the dataitself.

For researchers working on ﬁducial or partially ﬁducial approaches,segmentation isakey aspectinterms ofdata analysis.WereferthereadertothepapersbyBachleretal.[20]

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Fig.9–Informedconsentdocumentthatwasreadtoandsignedbythevolunteers.

andbyCanentoetal.[21],inwhichadescriptionand compar-isonofseveralECGsegmentationalgorithmsispresented.

6.2. StudieswithCYBHi

Inpartorinfull,thisdatasethasalreadybeenusedin sev-eralpeerreviewedandacceptedstate-of-the-artpublications. In[13]astudy ofdifferentsegmentationalgorithmsis per-formed, which addresses the problem of online vs. ofﬂine segmentationofhand/ﬁngersECGdata;63recordsfromthe

CYBHi short-termdatasetwereused,and theauthorshave concludedthatonlineapproachespresentcompetitiveresults bothintermsofthenumberofoveralldetectedsegments,and onthepercentageofthosethatcanbeconsideredtobevalid. In[12], astudyon thebiometric performanceoffullvs. partialwaveformispresented.Atotalof32recordsfromthe CYHBi short-term dataset were used, to evaluatehow the useofthewaveformcomprehendingtheP-QRS-Tsegments compares tousing onlythe R-STportionofthe waveform. Experimentalevaluationwasperformedbothondatafromthe

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Fig.10–Participantgiftcardofferedtovolunteersenrolledintheexperiments,showinghighlightsoftheirownsignals.

dryAg/AgClelectrodesandElectrolycras,andresultsrevealed thattheP-QRS-Tprovideshigherrecognitionratesduetothe additionalinformation;inthebestcasescenarioa1.66%±2.28 identiﬁcationerror(EID)wasachievedforgroupsof2users. Nonetheless,withamarginaldecrease inperformance,the RS-Tsegmentbasedapproachisabletoprovideanadequate compromisesolution,giventhatithasalowertemporaland spacialcomplexity;forgroupsof2users,thisapproachhas revealed2.40%±3.96EID.

Targetingthereal-timeclassiﬁcationscenario,in[9]atotal of32recordsfromtheshort-termCYBHiwereused,to com-paretheperformanceofaNearestNeighborclassiﬁerwithan

approachbasedonSupportVectorMachines(SVM). Experi-mentalresultshaveshownthat,dependingontheapplication, each approachhas its own advantages,and that the SVM basedapproachenableshighrecognitionrates,forexample,in anauthenticationtask,foranoperatingpointwheretheFalse AcceptanceRate(FAR)is0%,theFalseRejectionRate(FRR)can beaslowas13.91%±4.55.

In[22],twoPCA-basedmethodsarepresented,whichuse the fullCYBHi short-term dataset to explorethe concepts ofIndividualEigen-HeartbeatandOverallPopulation Eigen-Heartbeat, asamethod offeatureextractionand template creation.Resultshaveshownthata0%EIDcanbeachieved.

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Fig.11–Promotionalbannerscreatedtosignalthe initiativetoprospectiveparticipants.

Fig.12–Multimediasetupforemotionelicitation.

In [23,24], the authors tried to cope with outliers and time-variance introduced by acquisitions over time using wavelets and clustering-based methods. Using 63 records, andthetwotemporalseparatedsessionsfromthelong-term CYBHi dataset,Equal Error Rates(EER) of11.5% and 12.4% wereachieved,revealingthatthisisachallengingtask.More recentlyin[11],wefurtherevaluatedthetemporalstabilityof ECGsignalscollectedattheﬁngers,reachinganEERof9.10% usingSVMsalsofortheCYBHilong-termdataset.

7. Resources

7.1. Materials

Allmaterialsandoutputsofthedataacquisitionsessionswere groupedintoafolderstructure,whichcanbeusedforfuture

Table7–Acquireddataﬁlestructure.

Col. Content

1 7-bitsamplesequencenumber(fromthedevice) 2 Digitalinput(fromthedevice)

3 Digitaloutputtoggle(fromthesoftware) ≥4 Analogchannels≥1(fromthedevice)

reference. Theseincludegraphicmaterials,videos, the

col-lecteddata,andsupportdocumentation;Table6outlineswhat

canbefoundineachfolder.

7.2. Dataformat

Eachfilecontainsthedatacollectedfromthebiosignal acqui-sitionandsynchronizationsensors.Includeddatacomprises themodalitiesdescribedinSection3;thedifferentmoments oftheexperimentalprocedureacquiredwiththetwo biosig-nal acquisition units are stored in separate files. Files are identifiedwithindicationofthedate,acodeassociatedwith thesubject,andthemomenttowhichtheycorrespond(e.g. YYYYMMDD-AAA-BB-CC.txt),thegeneralnotationbeinggiven by:

<filename> ::=<date>“−”<code>“−” <moment>“−”<unit>“.txt” <date> ::=YYYYMMDD

<code> ::=up to three characters <moment> ::=“CI”|“A0”|“A1”|“A2” <unit> ::=“8B”|“85”

Intheabovenotationusedtodistinguishthemoment,“CI” denotestheinformedconsentpartofthetest,while“A1”and “A2”denoterespectivelythelowandhigharousalvideosinthe short-termdataset;“A0”denotestheacquisitionsofthe long-termdataset.Inthenotationusedtodistinguishthebiosignal acquisitionunit,“8B”and“85”refertothe“00:07:80:42:0F:8B” and “00:07:80:42:0F:85” units,respectively. Data isstoredin standardASCIItextwithabioPLUXﬁleformat,whereeachline correspondstoonesample;thecolumnsmappingisdescribed inTable7.Theheaderhaseightlines,precededbythe# sym-bol,andthestoredinformationisthefollowing:

# bioPlux Text File Format # Version: 1 # StartDateTime: YYYY-MM-DD HH:MM:SS # SamplingFrequency: 1000 # SampledChannels: 1 2 3 4 5 6 7 8 # SamplingResolution: 12 # AcquiringDevice: XX:XX:XX:XX:XX:XX # EndOfHeader

Table6–Descriptionoftheprojectresources.

Folder Content

data Rawdatacollectedduringtheexperimentsstoredinindividual.txtﬁles

documents Supportdocumentationusedintheexperiments:informedconsent,participantlist,andacquisitionsetupspeciﬁcation

graphics Graphicmaterialspreparedfortheexperiments:bannersandgiftcards

photos Picturesofdifferentaspectsoftheexperimentalsetup

results Graphicsanddataﬁleswithresultsfrominitialdataprocessing

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8. Lessons

learned

WhiledatasetswithECGcollectedatthechestusing clinical-gradeequipmentare readily available,the samecannot be saidregardingsignalscollectedatthehandpalmsorfingers inanoff-thepersonapproach.Amongotherpotential applica-tions,theseunconventionallocationsarefundamentalforthe growingresearchfieldofECGbiometrics,asthechest place-mentisfartoointrusiveforfeasiblereal-worlddeployment. ThesenoveltrendsinECGbiometricsrequiremoreconvenient signalacquisitionmethodshowever, inthisparticulararea, researchersstilllackpubliclyavailabledatasetsthatcan pro-motebenchmarkingandcomparisonofresultsacrossteams. TheCheckYourBiosignalsHereinitiative(CYBHi)dataset, provideshighresolutionrawdatafromsignalscollectedatthe handpalmsandfingersinanoff-the-personapproach,using dryAg/AgClelectrodesandElectrolycrasasinterfacewiththe skin.Wehavedescribed abiosignalacquisitionframework, devisedtoovercometheneedforarepeatableand easy-to-applyprocedurefordatacollectioninlargepopulationgroups. Thissetupbuildsuponpriorworkfromourgroupinthearea ofbiosignalresearch,asatooltoidentifypotentiallytime vari-antpropertiesinthesignals,andtoevaluatetherobustness ofalgorithmsandmethodstochangesintheenvironmental conditionsandotherdataacquisitioninfluencingfactors.

Westartedtocompileashort-termdatasetwhereasingle acquisitionsessionwasperformedtocollectsignalsthatcan comparedifferentelectrodematerials,aswellastheeffectof arousal-elicitingstimuliintheheartbeatwaveformtemplates. Along-termdatasetwasalsostarted,inwhichtwodata acqui-sitionsessionswereperformed,enablingtheevaluationand benchmarkingofthepersistenceoftheheartbeatwaveform templatesovertime.Several studieshave alreadyusedthe CYBHidatasetandprovidedvaluableinsightonthepotential ofthecollecteddata,aswellasguidelinesonhowtoperform pre-processingofthedata.Ourdatasetisalreadymade avail-ableforresearchersworldwide.9_Furthermore,_we_also_created

asetoftoolsthatresearcherscanusetofurthercontributeto theCYBHidataset,andreplicatetheexperiments,in particu-lar,byprovidingaccesstothebiosignalacquisitionhardware itself.10

9. Future

plans

Ongoingworkisfocusingonfurtherextendingthenumberof subjectsbothintheshort-termandlong-termdatasets,and alsoenrichingthepotentialofourdatasetbyrecallingthe sub-jectsthatwerepreviouslyassessedinordertohavearecord ofthecardiacactivityofeachsubjectindifferentmoments intimeseparatedbyseveralmonthsoryearsapart.Weare currentlyrecallingthepopulationenrolledintheCYBHi long-termexperiments,inordertorecord1-yearapartdata.

Inparallel, we are also workingto provide morphologi-calground-truthdatabymanuallabeling ofﬁducialpoints inthe recordings,whichwillextendtheuse ofourdataset

9 _{http://camoes.lx.it.pt/CYBHi.rar}_. 10 _{http://www.bitalino.com}_.

beyondtheﬁeldofECGbiometrics,enablingforexamplethe assessmentandbenchmarkingofﬁlteringandsegmentation algorithms[25–27]. Anotheraspectthat willbeanalyzedin futureresearchworkisthecorrelationbetweentheECG sig-nalsandthevariationsofthearousallevelofeachsubjectas measuredthroughthecollectedEDAdata.

Regardingthedatasetitself,wewillseekthepossibilityof makingitavailablethroughPhysionet[7]inaneartime,and makingitcompliantwiththeISOstandardsforbiometricdata storageandexchange.Finally,thedatasetisnowprovidedas standardASCIItextﬁles;futureworkwillalsofocuson migrat-ingthedatasettoastructureddatabaseformat[28–30].

Acknowledgements

ThisworkwaspartiallyfundedbytheFundaçãoparaa Ciên-ciaeTecnologia(FCT)undergrantsPTDC/EEI-SII/2312/2012, SFRH/BD/65248/2009andSFRH/PROTEC/49512/2009,bytheIT –InstitutodeTelecomunicaçõesunderthegrant“Android Bio-metricSystem”,andbytheDepartamentodeEngenhariade ElectrónicaeTelecomunicaçõesedeComputadores,Instituto SuperiordeEngenhariadeLisboa(ISEL), whosesupportthe authorsgratefullyacknowledge.Theauthorswouldalsolike toacknowledgeJoanaSantosandAnaTabordafromtheEscola Superior de Saúde da Cruz Vermelha Portuguesa for their invaluablesupportinpartoftheexperimentaldataacquisition forthelong-termdataset.

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