Classifying Multichannel ECG Patterns with an Adaptive Neural Network.pdf

7/28/2019 Classifying Multichannel ECG Patterns with an Adaptive Neural Network.pdf

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S. Barro, M. Fernandez-Delgado,J.A. Vila-Sobrino, C V. Regueiroand E. SanchezDept. Electronics and Computing,

Universityof Santiago de Compostela

Classifying Multichannel ECGPatterns with an AdaptiveNeural Networkn this article we describe the applica-I ion of a new artificial neural networkmodel aimed at the morphological classi-fication of heartbeats detected on a multi-channel ECG signal. We emphasize thespecial characteristics of the algorithm asan adaptive classifier with the capacity todynamically self-organize ts response tothe characteristics of the ECG input sig-nal. We also present evaluation resultsbased on traces from the MIT-BIH ar-rhythmia database.

Importance o f Class i fy ingth e QR S ComplexAnalysis of the ECG signal is of greatimportance in the detection of cardiacanomalies. One of the most importantsignal intervals in this respect is the QRScomplex, which is associated with elec-trical ventricular activation. Besidesother information contained n this signalsegment, its principal application is to befound in the important modifications thatits morphology undergoes in beats ofventricular origin. Consequently, themorphological classification of QRScomplexes (henceforth QRSs) consti-tutes an important step in the detection ofventricular arrhythmias, the solution ofwhich, apart from constituting anotherpattern-recognition problem, has certaincharacteristics that should be described.First, the number and varietyof the possi-ble morphological classes is practicallyinfinite. The appearance of heartbeatsvaries considerably, not only among pa-tients, but also as the consequences ofmovement, modifications in the electri-cal characteristics of the body, etc. Thus,it is not possible to construct a trainingset that is representative of all the possi-ble morphologies.Onthe other hand, theECG signal fre-quently presents noise components ofvarious origins; among these one couldpoint out the biological ones, such as, forexample, the movement of patients mus-cular masses, which generates high-fre-

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quency noises, or respiration, which mayprovoke baseline wander. Other noisecomponents have electrical (network in-terference) or mechanical (changes in theconnection of the electrodes on the pati-ents skin) origins. These are contributingfactors to the noise level of the signal overa time period, and they bring about theneed of a certain capacity for the evalua-tion of this noise, and, consequently,modificationof the classification process.Other aspects to be considered arethose associated with the simultaneousprocessing of various signal channels. Inthis case, there is an increase in the infor-mation available, improving the capacityof the system to detect events (such asQRSs) which do not showup(or dosoin adifferent way) in some leads. A ventricu-lar beat, for example, may present a prac-tically normal morphology in somechannels, while being clearly aberrant inothers. For this reason, the considerationof just one channel could more easily leadto an erironeous classification. On theother hartd, multichannel processing im-proves irnmunity in noise situations, asthere is a greater possibility of there beinga channel with high signal quality.All these considerations have, in oneway or another, led to the numerous ap-proaches which, in recent years, havebeen followed in the classification ofQRSs. Before commenting on some ofthese, we should point out that in manycases they consider classification pro-cesses aimed at discriminating among dif-ferent morphologies. On the, contrary,most work has been done on discrimina-tion among cardiac arrhythmias. Assum-ing, then., a broad context when speakingof the classification of QRSs, we can dis-tinguish, in the first instance, betweenso-lutions that make use of the signal samplesthemselves (template-matching classifi-ers [7, 20]), and those that are based onfeatures extracted from those signals (fea-ture-extraction classifiers [11, 23 ). Wecould mention certain advantages associ-0739-5175/98 /51 0.0001998 45


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ated with the first group, such as the stor-ing of morphological representations ofinput patterns, whicharcof great clinicalvalue in the control and follow up of thepatient. Among the inconveniences, wecould mention the high amount of compu-tation time required for comparisons withthe different templates learned, along withthe temporal alignment difficulties be-tween these and the input QRSs.The ap-proach that we put forward in this workfalls into this type of technique, but dealswith some of these inconveniences bymeans of an adaptive, self-organisingneural structure.Those classifiers based on the extrac-tion of features [171 usually have a deci-sion-tree structure or a set of rules (attimes fuzzy [SI) that determine the resultof the classification by means of compari-son of the aforementioned features withcertain thresholds, normally determinedempirically. Other classifiers are inte-grated in a syntactic classification proce-dure ([19, 21, 221) based on a set ofgrammatical rules that operate on chainsof primitive input pattern descriptors.Among the advantages of these tech-niques, one could mention that they re-duce template temporal alignmentproblems, although calculation of the fea-tures also depends, to a certain extent, ondetermination of fixed points in the ECGsignal. Among the inconveniences, weshould point out that the efficiency ofthese techniques depends completely onthe representativeness of the selected fea-

tures and also on the parameters (thresh-olds) used in the rules or decision trees.Lastly, on occasion, it is important topoint out the complexity added to the pre-processing by the calculation of features.Artificial neural networks (ANNs)have recently been used in the aforemen-tioned tasks, primarily multilayer percep-trons (MLPs). In [24], a network of thistype is presented for the discriminationbetween normal and ventricular beats. In[181,the network input consists of diversefeatures for discriminating between nor-mal rhythms, ventricular hypertrophies,and myocardial infarcts. In [16], a multi-layer perceptron is used for discriminat-

_ _ _ _ {ukFirstComparison -Detection ~ I

Block 1{nick)

Selection ofClass toInitializeA

1 I inic

1.Block diagram of M AR T .46 IEEE ENGINEERING IN MEDICINE A ND BIOLOGY

ing between normal, ventricular, and fu-sion rhythms, using the first coefficientsfrom the analysis of principal componentsonECG signals. This latter system has theadded complication associated with thecalculation of these coefficients. Otherexamples of supervised networks can befound for rhythm discrimination, such as[15] or [2], in which various structuresthat employ radial base functions areused. The input data are fuzzy descrip-tions of a series of features (fundamen-tally wave amplitude, duration, and area)extracted from theQRS complex and theT-wave, and a discrimination processamong seven diagnostic classes is carriedout. To the complications that these solu-tions have in the need for prior feature ex-traction, we have to add complicationsassociated with supervised learning. Suchlearning is not very flexibleand informa-tion coming from multiple ECG channelsis not included, which is something wehave tried to remedy in our proposal.In our case, the reason for using a clas-sifier based on a neural network is in re-sponse to the special features of thesesystems with regard to the highly paralleland distributed processing that they carryout, and also their modular design, par-ticularly useful in the caseof the multi-channel classification that we aim to carryout.Wehave chosen the ECG signal itselfas the input data to be fed nto the network,with the idea of using all the morphologi-cal information contained therein, and notonly that which shows up in a certain setof features. In this way, the efficiency ofthe classifier is not conditioned by the rep-resentativity of the chosen features, andby the same token, the preprocessingstages aimed at their calculation areavoided. The assignation of an input(QRS) pattern to a specific class is ef-fected by measuring the similarity of therepresentatives (templates) of thoseclasses that have been learned by the sys-tem, making up the information of the dif-ferent ECG signal channels analyzed.Lastly, from amongthedifferent types ofneural architecture, we have chosen a net-work which, like adaptive resonance the-ory networks (ART) [3, 41, given thepeculiarities of the problem that we havedecidedtoaddress, employs unsupervisedon-line learning, and which enables directstoring (and recuperation) of the mor-phologies learned (a feature that is absentin other models such as MLPs). On thisbasis, we have included substantial modi-fications aimed at simultaneous and selec-

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tive processing of multiple ECG channels,endowing the classifier with a great ca-pacity of adaptation to the dynamc char-acteristics of the input pattems. We havedescribed our architecture, which we callMART, and the way it works, from thepoint of view of a general approach formultichannel pattern recognition, in greatdetail in another paper [9]. In this article,we focus solely on those characteristicsspecially incorporated for the morphologi-cal classificationof QRS.

A Neural A rchi tecture ForMul t i channel Pat tern Recogni t ionThe neural network that we have de-signed is based on ART, developed byCarpenter and Grossberg([3,4]),althoughit has been considerably modified with theobjectiveof carrying out an adaptive andself-organising classification of multi-

channel patterns. Wecall this new neuralnetwork MART (Multichannel-ART). Inthis section we introduce its structure andgeneral characteristics, analyzing the dif-ferent learning capacities that it possesses.For each channel, the input data fed intoMART are samples ofECG signal corre-sponding to a time window with a dura-tion of 250 ms, distributed in the 100 msprior to, and the 150ms after a fiducialpoint identifying the position of eachQRS. This time period is sufficient tocompletely include each QRS complex.Prior to acomplex being introduced intothe network, a scaling or amplitude nor-malization process sets its maximumvalue at 1 0 and the mnimum at 0.0.Figure 1 shows the block diagram ofMART. One can make out a set of Iblocks, each being associated with adif-ferent signal channel. The remaining no-

F5i

2. Close-up of the structureof asingle-channel block.J anuary/Februory 1998 IEEE ENGINEERING INMEDICINE AN D BIOLOGY

menclature will beexplained as we prog-ress through the explanation. Figure 2shows the diagram of the block associatedwith channel i. The input data,EB? =1,.., , ( J is the number of inputunits in each channel, in our case isJ =125) are presented to the unitsF l,,j =1..., ,spreading toward the unitsF2,,,k = 1...,K (K is the numberof mor-phological classes that MART is capableof learning). The unit F2,, is associatedwith the morphological classk, in such away that the weights z j i k , =1...,J , storeits template (adaptive averageof thepat-terns assigned to classk,which acts as itsmorphollogical representative) in thechannel i, as is graphically illustrated inFig. 3.The output,&& =1...,K , rom theunitF 2,k s given by the expression:

(1)Likmeasures the dissimlarity betweenthe input and themorphological templaterepresentative of classk in the channel ,using for this the "city block" distance he-tween them. This is a measurement thathas a low computational cost, simlar tothe human comparative method, and thathas been used in previous approaches forthe classification of QRSs [6].The princi-pal inconvenience is its high sensitivitywhen faced with temporal displacementsin the QRSs input window. In fact, thisphenomenon is a frequent cause of the in-correct reproduction of classes actually

1Yk -min { ' d k }'zk =:g y-,= { z , /k>- mni{zdk}'1= , ...,

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representing the same morphology.Lastly, we would mention that the classtemplate, due to it being updated witheachQRS (a process about which we willgive more details at a later point), is notnormalized between 0 and 1, since it isnecessary to include this normalizationprocess in the calculation of the dissimi-

larity between the template and the inputpattern. The output7;, of the unitF3*, as-sociated with the classk, is:

(2)

Template 1 F2, Template K

IInput Pattern Y In Channel i

3.The storing of the morphological templates learned in connections in channelblock i (zVk, =1,..., ; k=l, ..K) for 1and K . Unit F2i1, associated with the winninclass 1 after comparison with the input pattern, isshaded.. . eU1+ II inic

...........

F4

K )e . .

T11 TI1

. . .4. Structure and connections of block F4.48 I E EE E NG INE E R ING IN M E D IC INE A ND B IO L O G Y

This output is zero for those unitsrepresenting unlearned classes andthat are still not initialized (qi =0 forthese units). For the ones associatedwith classes that are already initialized(qk= l),Tkmeasures the similarity be-tween the QRS and the templateof classk. The output,T.krk=1..., K, re fed intoblockF 4 (see Fig.4). Inthis block, eachline,P,


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New Inputomplex-

Selectionof Classk with MinimumClass C redit

I I Match with

Calculation of Selection ofGlobal Similarities Unreset Class k*(Pk) with Pk* Maximum

Assign InputQR S Complex.o Template k Class k*LT1pdatingofI emplate k*I5.Flowchart of classification in M A R T (for simplification pur poses, some stages have been left out, such as the updating ofglobal vigilances and channel credits).show smaller differences compared withthe morphological templates to whichthey are associated.

Additionally, this block stores someparameters, known as global vigilances(pf, k =1...,K), that are associated withthe different classes learned. For a givenclass, this parameter quantifies the globaldifference, d, which is tolerated byMART when the class is selected by thecompetitive process inF4. MART com-paresd with the global vigilance associ-ated with the winning classpi,, in such away that if d


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Class/ emplate CurrentTemplatePreviousTemplate

Ilate

ClassRadiu/ / TemplateCurrentTemplate7.Development of the template and the class radius, in the I J -dimensional nput space, in the caseof gradual changes in themorphology of the beats that it represents (for simplicity we have used a 3-dimensional representation).worthy aspects, which we will go on todeal with.L earning of morphologies. MARTuses a nonsupervised learning processwith regard to the morphologies that arepresented in the input data, storing a rep-resentation of each one of them (templatesrepresentative of the different morpho-logical classes). An input beat may matchan already initialized class, or it may ini-tialize a new one. In the former, the tem-plate of the matching class is updated byemploying a weighted sum of the tem-plate and the input beat (this processusu-ally leads to the fact that the templateceases to be normalized between0and1as we commentedonwhen looking at Eq.(1)).The associated weighting of the beatis notably lower than the one correspond-ingtothe template, with the aim of assur-ing agradual evolutionof the latter, andattenuating the consequences of possibleclassification errors.In the case of initiali-zation of a new class, the input pattern willbe assumed as its initial template.L earning of channel credits. Initially,all the channels possess the same credit (insuch a way that the sum for all the channelsis the unit),-,with this being understood asthe contributionof each channel to the finalclassification of the input pattern. Witheach input pattemto be classified,thelocaldifference,d, s evaluated n the first com-parison (as t is the one that is associatedwith the class most similar to the beat),without taking into consideration theweighting by meansof the channel creditsx, themselves. The First ComparisonDetection block (Fig.1)activates its out-put during this stage. A lowd,enables us toassume that the channel has, at this mo-ment, a good signal quality, which is usedto increase its channel credit. [The way in

1 .I

which the morphological templates areupdated enables the elimination of mor-phological distortion due to noisesof van-oustypes (see Fig.6),and due to which onecan assume that input pattems very similarto these are being obtamed from a channelwith a good signal/noiseratio.] In the con-trarycase, it operates n an inverse manner.The increase/decreaseof x, s produced insmall quantities, in such a way that its evo-lution is determined by tendencies that aresustained over significant time periods, be-ing approximatelyx,constant when facedwith isolated events. The variation ange ofthe channel credits is an interval whoselength ncreases with the average of the lo-cal differences, nsuch a way that the vari-ability of the channel credits is greater inthose channels with lower signal quality.

The learning process for global vigi-lances. As previously discussed, MARTdemonstrates a capacity for the dynamicdiscrimination of morphological classes,by way of a grade of tolerance in the as-signation of input pattern to classes,which is adaptive for each class. Thesetolerances are the global vigilmces, pstored in the Orientation block, whosevalues determine the maximum differ-ence permitted by MART in order that anassignation to each one of the classes becarried out. This process deals with thenotable difference with respect to theART model, in which the vigilance isunique and constant throughout the proc-essing stage. [In [12] a fuzzy ARTMAPnetwork [5] is described for the single-channel discrimination between normal

8. Signal interval corresponding to register 105in which complexes of great vari-abil ity are shown.

50 IEEE EN GINEER IN G IN MED IC IN E AN D B IOL OGY J anuary/February 1998


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and ventricular QRSs. For this network,the first two coefficients of the linear pre-dictive codification and the average quad-ratic value of the QRS complex are usedas inputs. The vigilance value,pa, or theART, module is optimized by means of su-pervised training prior to the on-line op-eration, during which p will remainconstant.] The initial values of pfare thesame for all morphological classes and aredetermined in a stage of supervised train-ing prior to on-line processing (based onthat used in ARTMAP [5]). or each QRSclassified during on-line processing, thevigilance of the class with which it finallymatches s updated. This updating processis a function of the radius,5,of the class,which is a parameter that evaluates anadaptive average of the global differenceswith theQRSsassigned to it. By employ-ing a representation of the input to MARTin the IJ -dimensional input space, theQRSs assigned to a class are arrangedaround a specific point associated with themorphological template of this class. Bymeans of these mechanisms, MART hasthe flexibility necessary to adapt itself togradual changes in the morphology of in-put pattems, with the aim of avoiding theindiscriminate creation of new classeswhich are, to a great degree, redundantwith regard to the already existing ones.Figure7 illustrates a possible evolution ofthe template and the radius of a class in theIJ -dimensional space as the consequenceof a gradual modification n the morphol-ogy of the input beats. In the intermediatestage(t=n), the change in the morphol-ogy of thebeats gives rise to an increase ntheir global differences with an old tem-plate and in the radius of the morphologi-cal class, at least while this template,whose evolution s gradual, does not adaptto the new morphology. When this adap-tation finally comes about(t =m), he dif-ferences and the radius arc reduced again,retuming to a situation similar to the ini-tial one, but with a different template rep-resenting the class.The updatingof theglobal vigilance,pof a class is carried out by comparing theglobal difference,a', with its radius,5. f dis higher,p i ncreases in a fixed, but lesseramount, and decreases f dis lower. In thiswayfmirrors the behavior of G,althoughalwaysmaintaining a valuehigher than aminimum,p identical for all classes, asdetermined by prior supervised training.This value represents the maximum dis-criminationcapacity that a class may have.

0.20.1

As happens with the channel credits, thevariations n global vigilance are minor,sothat only a sustained tendency in the inputcauses substantial variations in the globalvigilance. By way of this mechanism, n asituation as that shown in Fig.7, the resultof the morphologicalchange of a class isanincrease not only inr,,but also n@: greater

--Ii--

differences in the comparisons with thisclass are tolerated. Thus, the class capacityfor beat capture is increased n an attemptto avoid the creation of new redundantclasses. In the final stage(t=m),the dif-ferences again become minor, with r, andp decreasingsoas to readapt the system'stolerance to the variability shown by thebeats of this class.

Elimination and creation of classes.MART has a limited learning capacitywith regard to the maximum number ofclasses that it stores at a given moment.When a bleat with anew morphology s fedinto the network, it will not normally bematched with anyof the existing classes.In these cases, if the number of classeslearned by MART is lower than a maxi-mum (in which case its learning capacityhas not been totally used up), the result isthe initialization of anF 4 unit, creating anew clasis for the new morphology. If thecapacity of the system is full, one of theclasses already earned will be eliminated,following a criterion of morphological ir-relevance, which selects the class withlowest credit. When a class is created, itscredit,p k , s assigned the valueof 1O andthen updated with each beat processed, n-

I L I

9. Development of global vigilance (p;) and radius rl of class 1during the process-ing of register 123.

Channel 1xi-- 0. 5Channel2x 2

d2Initialized C lasses

0. 5

Time-I10. Development of channel credits,x i ; local differenicesd(i =1,2);and the numberof classes,N , for register 105 (see text for full description of figure).

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Trace105106108

net gross net gross67.34 99.53 99.00 99.5361.53 94.97 62.13 93.3497.22 99.94 99.99 99.94

114119123

99.36 98.1 1 67.59 99.95 1.89100.0 100.0 100.0 100.0 0.0100.0 100.0 100.0 100.0 0.0

200201

62.49 96.85 74.42 96.21 3.1599.98 99.95 99.83 99.95 0.05

202203

83.33 99.95 98.15 99.95 0.0546.14 93.96 50.31 95 55 6.04

20520821021 3

~

10000 1000 100 0 100.0 0.097.82 97.53 98.73 97.63 2.4766.13 98.1 7 70.59 98.18 1.8399.55 99.93 99.96 99.93 0.07

21421 721 9221228

50.00 99.22 49.61 98.44 0.7868.98 97.29 60.29 96.47 2.7199.95 99.91 98.48 99.91 0.0974.99 99.83 77.83 99.81 0.1773.33 99.85 79.49 99.80 0.15

223Avg

100.0 100.0 100.0 100.0 0.082.41 98.72 84.22 98.78 1.25

creasing withamatch or decreasing in thecontrary case. In situations of noise, thismechanism contributes to the eliminationof classes created in response to artifactssimilar to QRS complexes, which mayhave passed the previous beat-detectionstage. In fact, given the random nature ofthe noise components, the sustained appa-rition of artifacts with high similaritiesamong them is highly improbable, whichresults in a low update frequency of thatclass, arapid decrease n class credits, andits medium-term elimination.We should point out that the learningcharacteristics described have the objec-tive of endowing MART with the flexibil-ity necessary to carry out ECG signalprocessing over prolonged periods oftime. It is especially appropriate for ECGmonitoring (a less application-specificversion of MART [9, 201,was applied inorder to illustrate its capabilities in themorphological classification of pattemsin ECG signals that were seriously con-taminated.by noise in some channels. In

ECG processing, these situations are rela-tively frequent in stress tests due to mus-cle noise, movement of electrodes, etc.).Evaluat ion of the Q R SMorph olog ical Classi f icat ion ProcessTo evaluate the performance ofMART as a morphological classifier ofQRSs, we have implemented it in the C

programming language on aPC OlivettiM6-6200 with a Pentium-Pro 200 M Hzmicroprocessor running the Solaris x86operating system. UValidation SetThe validation set was taken from theMIT-BIH ECG arrhythmia database.The main reason for choosing this setwas the high numberof ventricular beatsthat it contains, making it especially ap-propriate for our purposes. Each traceismade up of two 30-minute ECG chan-nels. The sample frequency is 360 Hz,

which we converted to 500Hz prior toprocessing the signal. A lthough the

main characteristics of MART are basedon its capacity for multichannel analy-sis, the aim of presenting the resultsfroma standard database ledus to limitthe evaluation to just two channels. Nev-ertheless, withagreater number of chan-nels in our own traces we have alsoobtained positive results.The MIT-BIH database gives annota-tions in functionof the activation focus ofeach QRS complex (normal beat (N),pre-mature ventricular contraction (PVC),nodal escape beat (i),...) and its morphol-ogy, codified by means of numeric codesfor each class (subtyp annotation fieldwhich takes the values0,1,... ). In spite ofthis being an experimental morphologicalabeling, it can be consideredasan accept-able starting point for the validation of amorphological classifier for QRSs. Wechose20traces from the database, exclud-ing those that did not include ventriculaclasses. In almost all traces, the qualityofthe signal was fairly low, principally inchannel2, to the point that in some traces(for example in trace number 200) theQRSswere practically undetectable n thatchannel. This poor quality causes highvariability among beats belongingto thesame morphological class (Fig. 8), whichduring the classification process can resultin a large number of mismatchesand theinitialization of classes that are not in thedatabases morphological annotationsThere was considerable variabilitythroughout the processing, with intervalsin which beats belonging to the same mor-phological class showed very slight differ-ences among themselves as well asintervals when these differences were sub-stantial.By means of its adaptationmecha-nisms, MART is capable of tolerating ahigh degree of differences in classes of

Table 1. MART classification results fo r validation traces.

% error0.475.030.06

a17.252.5020.670.671.500.005.000.331.338.123.514.002.007.5014.501.502.01.832.61 o5.39

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great variability, so as to avoid the con-stant initialization of classes, requiringlow differences when the successive beatsare very similar among themselves.Asanexampleof the adaptive capacityof MART, Fig. 9shows the evolution ofglobal vigilance and the radius of class 1(q andpf), respectively, during the proc-essing of trace number 123.Here we cansee howpf develops, starting from its ini-tial value (we have used initial values ofpf =0.15Vk, andp =0.19, followingthe behavior of throughout the classifi-cation process, always above its mini-mum value.Similarly, Fig. 10shows the develop-ment of x, andd for trace 105.The chan-nel credit increases when the localdifferences are small and decreases as thedifferences increase. The developmentof the total number of initialized classes(number of classes created from the be-ginning of the trace) can also be seen,scaled down as a function of their finalvalues. The main increase of N (point(1))coincides with an increase ind,withthe creditx, being relatively high. This isa sudden degradation of the quality of thesignal in channel 1, at a moment when itsquality is considerably superior to that ofchannel2.MART adapts itself to the newsituation by means of a decrease in thecredit, x,, in response to the increase ind,.Nevertheless, the variation in the credits.x, , n the same way as inpf, is producedgradually. The presence of a certainnumber of beats with high d, s requiredsothatx, decreases in a significant man-ner. Here, the cause of the stabilization ofN at point(2) is not the evolution nx,, butrather the decrease in d, (and to a lesserdegree indJ. However, in point (3),onecan see MART's capacity for reducingthe proliferation of classes, since a newincrease in d, and d2 is produced, up tolevels of those similar to those of point(l),but with a smaller credit, 3. This isthereason why the increase inN is nownotably inferior. The same phenomenonis produced in point (4), in the samechannel, although now 3 again has ahigher value. Thus, we can conclude thatMART, to a great degree, reduces theproliferation of classes when faced withthe gradual degradation in the signalquality of a channel. The same occurs ifthis degradation is sudden, but it is pro-duced in a channel with little credit. Nev-ertheless the limitation in the speed of

response reduces MART's capacity foravoiding class proliferation when facedwith a sudden degradation n the quality ofa channel with high credit.V alidation Par ameter sThe validation of the morphologicalclassification of QRSs was carried outwith a comparison between the morpho-

logical labeling given by the MIT-BIHdatabase and by MART itself. For thispurpose, we evaluated two parameters,based on definitions ntroduced by AAMI[I], that quantify the ability of MART toreproduce the labeling in the database.They are as follows:. ensitivity( Se)of aclass. This is ob-tained from the quotient betweennumber of beats correctly assignedby MART to this class and the totalnumber that belong to it.. ositive Predictiveness (+P) of aclass. This is the quotient betweenthe number of beats correctly as-signed to this class and the totalnumber assigned to it.In the calculation of these parameters,the increase generated by MART in thenumber of classes with respect to thatgiven by the labelingof the database is ofgreat importance. In effect, if MART cre-ates various classes from beats belongingto an original one, all the beats assigned toany one of these, except the first one,could be accounted for as errors in the cal-culation of Seand+P. Nevertheless, theinitialization of additional classes does

not constitute, for us, a classification er-ror. In reality, and given the considerablemorphological variation of the beats ofone original class, any classifier with acertain discrimination capacity shouldseparate each original class into sub-classes formed by groups of beats with ahigh degree of morphological homogene-ity. For example, given a beat,L,belong-ing to the class N o (according to thedatabase labeling), its assignation byMART to a class N I (morphologicallysimilar toNo),will constitute an error ofless importance than its assignation to aclass,y, learly different fromNo. For thisreason, in the calculation of Seand+P,wehave considered unique all those classescreated from beats with the same mor-phol ogy, in such a way that thei r val uesarenot influenced by the problem of thesubdivision of classes. In any case, and inorder to give a measure of this problem,we have defined the multiplication rate of

classes for a trace, k, as: ak= -1N!iwhereM , is the number of classes gener-ated by MART, andN , is the one deter-mined by the database:a, s the number oftimes that MART increases the originalnumber of classes in the tracek.

IDiscussion of Resul tsIn this section, we present the quantita-tive results of the validation. Table 1shows, for each trace, the average valuesof Seancl +P per class, net (simple aver-age), ancl gross (the average obtained byweighting the contribution of each classwith its number of beats). The percentageof classification errors and the net averagemultiplication rate per class, a, is alsoshown for each trace. In the final row, weshow the net average of (Se)(net) and(+P)(net),and the gross average (calcu-lated by weighting each trace with its beatnumber) of (Se)(gross)and( +P)( gross) ,for all traces. The net average values of thepercentage of errors anda forallthe tracesare also shown. The low rate of classifica-tion errors (without taking into accountthe possiible class subdivision producedby MART) can be seen in this table. Theaverage net Se is over 90% in half of thetraces, and in those with lower values thegross valueishigher than90%,which in-dicates that those classes for whichSeor+P are low have very few beats. Thus,only a very small proportion of the total ofQRSs was incorrectly classified (in theimmense majority of traces, the percent-we can see that the average net values ofSe and+P are higher than80%,while thegross ones are over98%.These are resultsthat we consider favorable, bearing in

age of enorwasunder5%. In the ast row

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mind the low quality of the signal, princi-pally in channel 2.A nother aspect to analyze isMARTS efficiency in the multiplica-tion of morphological classes. In Table1, we can see thata s lower than five formore than a half of the traces, althoughthere are high values for some othertraces. The high degree of variability ofthe beats belonging, a priori, to the sameclass makes this growth in the number ofclasses almost inevitable, a situationthat could be accentuated as the numberof channels considered in the multichan-nel classification increases. One should,however, touch on the fact that thisproblem, although not negligible, gen-erally will have little incidence on pos-terior stages of ECG processing inwhich the information collected fromthe morphological classification ofQRSs is used. The identification of themorphological class of a complex is in-formation of special importance, for ex-ample, in the classification of beats inaccordance with the situation of their fo-cus of activation. In this case, the place-ment of a QRS in one class or another ofmorphologically similar classes doesnot condition, in principle, the finalclassification of the beat.

ConclusionsIn this work we have presented a neu-ral network, MART, and its applicationto the classification of morphologicalpatterns corresponding to QRS com-plexes of multichannel ECGs. This net-work is based on the ART ANN, amodelfrom which it inherits unsupervised on-line learning properties. Nevertheless,MART goes beyond this capability bymeans of the updating of those morpho-logical templates that match the inputbeats, the creation of new morphologi-cal classes as they appear, and the re-moval of those classes that remainobsolete. Additionally, MART adaptsthe discrimination capacityof each mor-phological class detected to the variabil-ity of those input patterns associatedwith it. The results demonstrate howthese adaptive characteristics contributeto reducing the problems of undesirablemultiplication of classes representingthe same morphological pattern, main-taining a high capacity for morphologi-cal discrimination. Another importantcharacteristic of MART is the selectiveevaluation of the beat-class differences

nal quality. This feature leads to agreater degreeof channel credit towardthe final classification of those channelswhich,, in principle, have greatersignal-to-noise ratios.The validation of this classifier hasbeen carried out on the basis of MIT-BIH ECG signals. The results obtainedare very positive, in spite of the low sig-nal quality that the database has in someof its traces and the high degreeof mor-phological variability in some of itsclasses. This QRS morphology classi-fier will be incorporated, as an interme-diate stage, into the search for the focusof activation of beats and the detectionof arrhythmias by an intelligent pa-tient-monitoring system (SUTIL+),which is being developed by our re-search group. Finally, we would like toadd that, besides the specific applicationpresented in this article, we believe thatthe use of MART is of interest in otherapplications dealing with multichannelpattern recognition. Thus, we intend toextend its use to other application areas.

AcknowledgmentWe would like to thank the CICY T fortheir financial support in carrying out thiswork under the project TIC95-0604.Senen Barro Ame-neiro was born in ACoruiia, Spain, in1962. He received theB.S. and Ph.D. (withhonors) in physicsfrom the University ofSantiago de Compos-tela, Spain, in 1985and 1988, respectively. He is a Profes-sor of Computer Science and head ofthe Department of Electronics andComputing at the University of Santi-ago de Compostela. Before joining thisuniversity in 1989,he was an AssociateProfessor at the Faculty of Informatics,University of A Corufia, Spain. His re-search focuses on signal and knowl-edge processing (mainly in medicaldomains), real-time systems, intelli-

gent fuzzy systems, and artificial neu-ral networks (applications andbiological modeling). He is editor ofthree books and author of over 60 scien-tific papers in these fields. ProfessorBarro is a member of the Spanish socie-ties AEIA, AEPIA and FLAT (of whichhe is a member of the managementboard) and the international societies

Manue F erncin-dez-Delgado was bornin A Corufia, Spain, in1971. He received theB.S. in physics fromthe University of San-tiago de Compostela in1994. He is a Pre-Doctoral research stu-dent in the Department of Electronicsand Computing and Ph.D candidate atthis university. His research fields areneuronal computing and intelligentmonitoring of physiological signals(mainly ECGs).

J ose Antonio Vila So-brino was born in 1968in Orense, Spain. He re-ceived the B.Sc. inPhysics in June 1991from the University ofSantiago de Compos-tela. Since November1992 he has been anAs-sistant Professor of Mathematics and Phys-ics Faculties of the university, where he isteaching undergraduate courses related tocomputer science and digital signal proc-essing. Healsoreceived the Ph.D. in 1997from this university. His research interesis in digital signal processing of biomedi-cal signals (especially spectral estimation).Carlos VcizquezRegueiro was born in1969 in Santiago deCompostela, Spain. Hereceived his B S.degreein physics in 1992 fromthe University of Santi-ago de Compostela.From December 1993to 1997 he was an associated professor atthe Faculty of Computer Science at theUniversity of A Corufia, Spain. SinceMarch 1997 he has been an assistant pro-fessor of computer science at the Univer-sity of Santiago de Compostela, where heis currently a Ph.D. candidate. His re-search interests include artificial neuralnetworks and mobile robotics.Eduardo M . ScinchezVilawasbomin Baxce-Iona, Spain, in 1970. Hereceived the B S.degreein physics from the Uni-versity of Santiago deCompostelain 1993. Heearned an M.S. in neu-roscience at the Interna-in each channel, as a function of the sig- AAAI , ACM, IEEE, and INNS. tional University of Andalucia, Spain, in

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1996.He is currently a Pre-Doctoral re-search fellow and a Ph.D. candidate at theUniversity of Santiago de Compostela.His research nterestarein neural comput-ingandcomputational neuroscience.Address for Correspondence: S. Barro,Departmentof Electronics and Computing,University of Santiago de Compostela,15706,Santiago de Compostela,Spain.

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