Classification of ECG Patterns Using Fuzzy Rules Derived from ID3-Induced Decision Trees.pdf

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Classificationof ECG PatternsUsingFuzzyRules Derived from ID3-Induced Decision TreesAmineM. Bemaid*+, NadiaBouhouch', Rachida Bouhouch",RoukiyaFellat" and Rachida h i "

AbstractIn cardiology, determning whether anelectrocardiogram(ECG) is normal or not is sometimesreferred to as ECG classification. ECG is the mostfrequently-used means of cardiac diagnosis. It is thecheapest and the most widely-available; it is alsocrucial for detecting rhythmc problems. In this paperwe derivefizzy rulesfor ECG classiJ ication rom ID3-induced decision trees. The system of jiuv rules isdesigned basedon106ECG's, and it is evaluated using avalidation set of 48 ECG's care&& selected bycardiologists. Using the same IO6 ECG's for design andthe same 48 ECG's for validation, an ID3-generateddecision tree yields 73% correct class@cations, and aneural network trained with the feed-fonuard cascade-correlation algorithm produces 85.4% correctc1assBcations. On the other hand, the derivedjiuzyrules, combined with an optimzed defirzrfication usingthe cascade correlation neuraE network, produces 100%correctclassifications.K ey words: ECG, ID3, fuzzy rules, defuzzification andneural networks.1. Introduction

Different approaches fiom pattern recogi iiicn,machine learning and expert systems have ben ilsed I Dintelligent diagnostic systems. Schi f f " et al. [11compared several classification algorithms using amedical data set; Rprop - an optimized version of thebackprop neural network algorithm - produced the bestresults, being surpassed only by the feed-forwardcascade-correlation (FFCC) neural network (NN).Dietmar [2] applied fuzzy logic in some medicalapplications, such as ECG analysis, drugkinetics andanesthesia monitoring.The decision tree-based approach, as a machinelearning technique, has been applied successfully toseveral practical problems [3]. Quintan's ID3algorithmand its variations [4,5] have been applied to various

classification problems because of their ease ofimplementation and the comprehensibility of thedecision trees. ID3-derived rules work well when theinput features have symbolic and discrete values.However, the ID3-based algorithms, which are symbolicin nature, are known tobe worse in modeling domains inwhich there is a 1zge number of continuous-valuedfeatures [6,7].For the classificationof ECG's, incorporating fuzzyconcepts into rule sets, could be a way to approach thesystem performance achieved by expert cardiologists.We use the algorithm proposed in [ XI for rulefuzzification; it is based on the ID3 decision treealgorithm and on a neural network-based optimized

defuzzification method. We produce fuzzy rules forECG classification. in Section 2, we present someprevious attempts to classify ECG's. Section 3 describesthe different algorithms applied in Section 6 to ECGclassification. The optimized defuzzification method isdescribed in Section 4. Section 5 outlines the structureof ECG data. Section 6reports the performance of ID3-derived fuzzyrules through experimental results. Finally,Section 7summarizes the conclusions.2. Some Previous Attempts at ECGClassificatisn

Several techniques have been proposed for ECGclassification. Grauel et al. [9] develop a fuzzy rule-based system for ECG interpretation using medicalexpert knowledge, but it remains useful for basicdiagnosis only. Vullings et al. [lo] developan algorithmto decide whether an ECG is normal or not. Thealgorithm first extracts features from the ECG using anunsupervised single-layerNN.Then it clusters the lengthof the separate features into several sets. Finally, areference set of features, classified normal by ananesthesiologist, s compared with a newly obtained setof features, using fuzzy clusters obtained from theprevious step. If t he similarity exceeds a certainthreshold, the signal is classified as normal. Preliminary

*CorrespondingAuthor, [email protected]+Graduate Computer Science Division,A Akhawayn University inIfrane (AUI), Schoolof ScienceandEngineering, ffane,Morocco.' ardiologieB, Hopital d'Enfants, Rabat,Morocco.' ardiologie A, Avicennes, Rabat, Morocco.

0-78034453-7/9810.000 1998 IEEE34


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results based on four patients indicate that this method ispromising for the problem at hand. In [ I l l Nsoutperform tvvo widely used rule-based interpretationprograms for classifying ECGs whose acquisitionsuffers from misplacement of electrodes during therecording of sunECG.3.Algorithims DescriptionIn this work, we seek a system (1) that learns to classifyECGs, (2) whose acquired knowledge can be explicatedthen verified by expert cardiologists and (3) with verygood classification performance. The differentalgorithms used to build such a system are describednext.3.1. ID3-Decision Treesand Rules

Quinlans ID3 [4] produces a classification tree bylearning from training examples. Each training pattemconsists of a set of m input features (F;, i =1,2, ...,m)and an associated class. Figure 1shows a decision treeproduced by ID3 for a four-input problem. Each noderepresents a feature Fi while each branch, f j, representsthej* interval of values of feature F;, which serves as atest. For a feature with discrete values, fUstands for thej* value of feature Fi. A leaf node, L p, contains aclassification C(L p). ID3 generates conjunctive rules.There are all together seven rules represented by thedecision tree shown in Figure 1.One of these rules (thehighlighted path) is:IF Fl(f13)AN:D F3(f31)AND F4(f41) THEN class is C(L,),where Fi(fj) is defined as

1, if feature5 has valuefC0 otherwiseq f j )After the tree is built (using, for example, a set ofECGs), a new pattern (e.g., a new ECG) can beclassified by starting at the top of the tree, answeringquestions and following branches accordingly until wereach a leaf, where the classification is stored. An ID3rule for ECG classification may look like:IF (the patient is resting AND the heart frequeuy isgreater than 60 and less than 80 AND the value offeature S bebongs to [0.3,1.5] AND the Amplitude of Twave is less than -0.5) THEN the ECG is abnormal.3.2. RuleFiuzzificationID3 rules work well when input data are accurate.However, their performance degrades when input dataare uncertain, noisy, or fuzzy [8]. Fuzzifying ID3-generated rules can be used to address the fuzziness inthe data and improve system performance. Generally,

fuzzy rule-based systems have been applied successfullyfor both control and pattem recognition [12]-[16]. Insome cases, fuzzy rules have been acquired from experts;in other cases, they have been produced by learning fromexamples [15,16]. However, when the number oftraining pattems and the number of features are bothlarge, the induced rule set is large and the IF-parts ofrules are long; consequently, classification becomescomputationally expensive. Fuzzifying the simpler ID3-induced crisp rules is an effective way to produce acompact set of decision rules.

Figure 1. A decision tree produced by theID3approach.Maher and St Clair [5] have presented a so-calledUR-ID3 algorithm to combine uncertain reasoning withthe rule sets produced by ID3 to deal with the uncertaintraining and test data. In their approach, the valueassociated with each branch of the tree is considered to

be approximate and soare feature values. A triangularly-shaped membership function is used for each of thesevalues. The classification of a test pattem is done byconsidering the corresponding set of support intervals foreach possible classif ication. Following Maher and St.Clairs idea to combine the fuzzy concept and ID3-decision trees, Chi and Yan [SI developed an approach tofuzzify simple rules derived from an ID3 decision tree toobtain a compact set cf fuzzy rules.I? m ID3 rule, anteczdent conditions are eitherTRUE or FALSE, and only one rule is chosen to performclassification. Following [SI, we convert these rules tofuzzy rules, such that a fuzzy rule may be (partially)applicable whenever a fact that partially hlfills itscondition is true. One of two different approaches istaken to fuzzify the crisp rules, dependingonwhether thefeature involved in the IF-part of the rule is discrete orcontinuous:Discrete features: for a feature Fi with L discrete

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values, an L*L fuzzy relational matrix is constructedto capture the similarity between the different valuesthat feature Fi can take on. For the purpose of ECGclassification, the similarity matrix is constructed bycardiologists. For example, for the featurePatient-Status that can take on one of five discrete values(A t-Rest, Changing-Position, In-Hyperventillation,Eating, Sleeping), the fuzzy relational matrixobtained is:

As a result, when for example, the patient is eating, arule whose IF-part is : "IF the patient is resting" ispartially applicable with degree0.4.Continuous-valued features: for a continuous-valued feature Fj, the shape of a piece-wise linearmembership function is constructed by cardiologists.This function description is then converted intomathematical equations. For example, consider thefeature Age that can take on "Newborn" as value.The membership function pFj(Newbom) forNewborn is described by:

(-0.1 * age)+1, if age 5 1day(-0.016 * age)+0.916, if 1day


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used, namely six extremity leads and six thoracic wallleads. These twelve leads are generally called standardleads. The ECG signals of these standard leads arepassed on to a feature extraction sub-system, wherecharacteristic features that are used by physicians forclassification ;ire calculated. This paper is not concemedwith extracting the features that describe an ECG; rather,it deals with determining the normality of an ECG givenits feature description.A normal ECG is composed of a P wave, a QRScomplex, and a T wave. T he P wave represents thespread of excitation in the atria. The QRS complex,which represents the spread of excitation in theventricles, actually consists of three separate waves: theQ wave, the R wave and the S wave. The T waveindicates the repolarization in the ventricles.We represent an ECG pattern with a set of 22 featuresthat were selected by three cardiologists as being mostrelevant for ECG classification. 18 of these features are

extracted from the ECG signal; the other four featuresare: patient-status, sex, age and position of the electrode.8 of these features are symbolic (e.g. patient-status) and14 are numerilc (e.g. dp: duration of the waveP).Cardiologists indicate that the interpretationof ECG'sis based on intervals of values for each feature ratherthan on the values themselves. Consequently, the rangeofnumeric values of a featureispartitioned into intervalsbased on normal values of that feature, as indicated bycardiologists. For example, normal values for the feature

dp are: dp 20.12 for adults and 0.041dp50.08 fornewborns. As a result, the numeric range for dp isreduced to four discrete values: dpl: [0, 0.04[, dp2:[0.04,0.08], 473: ]0.08,0.12], dp4: 10.12,+CO[.6. Experiments andDiscussion

The first step in our experiments consists ofgenerating a decision tree using ID3. The version of ID3we employ uses an information entropy measure to selectthe feature to be used at each node and construct the treeaccordingly. We do not employ windowing [21], CHI-square forward pruning [22], or any kind of reversepruning [23].. This accounts for the relatively largenumber of rules we obtain.Our experiments are based on 106patterns and the

corresponding class labels provided by cardiologists toform a training set Xd,along with 48 additional patternsthat make-up a validation set X'. Patterns in X' werecarefully chosen by cardiologists so that if these patternsare correctly classified by our system, cardiologists willbe able to assert with confidence that the system issatisfactory $or ECG classification. We carried out two

different sets of experiments using Xd: ne implementingthe leave-one-out strategy [24] and the other oneimplementing the iterative strategy [4]. These two setsof experiments were used to identify the subset of X d thatproduces the decision tree with the best performance.This procedure led to selecting a subset Xbc Xdwith 53patterns.ID3 trained with Xb produces a decision tree thatclassifies correctly all 106 patterns in Xd. We will referto the set of 80 rules extracted from this decision tree as

TID,. Applying these rules to patterns in the validationset X' results in about 27% misclassification rate. Theserelatively high numbers (both for the number of rules andfor the misclassification rate) may lead one to believethat ID3 is not generalizing as much as it could from Xb.For comparison purposes, an FFCC neural net wastrained using Xb. The trained network uses 46 hiddenunits and misclassifies4 out of the 53 patterns in (Xd -Xb); urthermore, t misses 14.6%of the pattems in X".The next experiment consists of fuzzifying the 80rules in TID3using the procedure outlined in Section 3.2.We will refer to the resulting set of fuzzyrules as TID3-F.Next, each pattern pj E Xb 6=1,2,4 3 ) s submitted toeach rule i (i=1,2,..:,80) in TID~-F,nd the corresponding0; s computed, as in Section 4. The resulting 53eighty-dimensional vectors


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an FFCC neural network using the same training setproduced 92.5%correct classifications on thetest set and85.4%correct classifications on thevalidation set. Thisempiri cal evidence indicates that ID3-derived fuzzy rulesare better than the straightforward ID3 for ECGclassifi cation. Furthermore, the fuzzy rule-based systemthat has been developed can reliably be used forscreening abnormal ECGs. Still, a smaller number ofrules is likely to be obtained if (prior to rulefuzzif ication) the ID3- induced decision treeispruned.References[l ] Schiff, W., M. J oost, and R. Wemer, Comparison ofOptimized backprop algorithms, n: M. Verleysen (ed.),European Symposium on A rtificial Neural Networks.brussels, pp. 97-104, 1993.121 Dietmar P.F. M oller. Fuzzy Logic in Medicine, EUFZT96,September 2-5 1996.[3] Kononenko, I., Bratko, I., E. Experiments n automaticlearningof medical diagnostic rules, Technical report.Josef Stefan Institute, L jubljana, Yugoslavia, 1984.

learning, vol. 1, pp. 81-106, 1986.ID3machine learning framework, in Proc. Second IEEEInt.ConJ FwiySyst.,San Francisco, CA, 1993, pp. 7-12.161Z. Chi and M. Jarbi. AcomparisonofMLP and ID3-derived approaches for ECG classification, n Proc.Second Australian ConJ:Neural Networks, Sydney,Australia, 1991, pp. 263-266.[7] T. G. Dietterich, H. Hild, and G. Bakiri, Acomparison ofID3 and backpropagation for English text-to-speechmapping,Tech. Rep., Dept. Comput. Sci., Oregon StateUniv., Corvallis,OR.[SI Z. Chi, and H.Yan. ID3-derived uzzy rules and optimizeddeffuzzi fi cation or handwritten numeral recognition,IEEE Trans. Fuzzy Syst.,vol. 4, no. 1, pp. 24-3 1, 1996.[9] A. Grauel, G. K lene, L . A. Ludwig, Rule-Based FuzzyLogic System for ECG Interpretation,EUFIT 97,September 8-1 1, 1997

[4] J . R. Quinlan. Induction of decision trees,Machine[SI P. E.Maher and D. St. Clair. Uncertainreasoning in an

[l o] E. V ullings, A .J . K rijgsman, and H.B . Verbruggen.Validating ECG Signals using anANN andFuzzyClusters,EUFIT 96,September 2-5 1996.[1 11Bo Heden, M attias Ohlsson, Lars Edenbrandt,RalfRittner, Olle Pahlm, and Carsten Peterson. A rtificialNeural Networks for Recognition of ElectrocardiographicLead Reversal,Neural Networks for AnalysisofElectrocardiogramspp. 929-933, 1995.[12] C. C. Lee. Fuzzy Logic in control systems: Fuzzy logiccontroller, Part I, IEEE Trans.Syst.,Man, Cybem., vol.20, no. 2, pp. 404-418, 1990.[13] C.C. Lee. FuzzyLogic in control systems: Fuzzy logiccontroller,Part 11, IEEE Trans.Syst.,Man, Cybem., vol.

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using self-organizing maps and huzzy rules,PatternRecognition,vol . 28, no. 1, pp. 59-66, 1995.[17] Rumelhart, D. E., Hinton, G.E., and Williams,R. J .L earning Internal Representationsby Error Propagationin Rumelhart,D.E.and McCleltand,J . L ., ParalleEDistributed Processing: Explorations in theMicrostructure ofCognition,MIT Press, 1986.[181 Fahlman, S. E. Faster-L earning VariationsonBack-Propagation :An empirical Study in Proceedings of the1988Connectionist Models Summer School, MorganK aufmann, 1988.[I91 Scott E. Fahlman and Christian Lebiere. The Cascade-Correlation Learning Architecture, In A dvances inNeural I nformation ProcessingSys.,2 (D.S. Touretzky,ed.), pp. 524-532. SanMatio, Ca :Morgan K aufmann,February 1990.[20] Pierre Godeau,Traite de Medecine, Tome 1,2ieme edition,Medecine Sciences Flammarion, Paris, 1987.[21 J .R. Quinlan. Learning eff icient classifi cation proceduresand their application to chess endgames, in M ichalski, R.S., Carbonell, J., and Mitchell , T. M ., (eds.),MachineLearning: An artiJ icial ntelligence approach, Vol. I, PaloA lto: Tioga Press. 463-482, 1983.[22] J . R. Quinlan. The effect of noiseonconcept learning. nM ichalski,R. S., Carbonell, ., and Mitchell ,T. M.,(eds.), Machine Learning, Vol. 11, Palo A lto: Tiago Press.149-166, 1986.[23] J . R. Quinlan. simplifying decision trees. nternationalJ ournal ofMan-Machine Studies, 27,221 234, 1987.1241 Devijver, P. and K ittler, J. Pattem Recognition: AStatisticalApproach, Prentice-hall, Englewood Cli ffs, NJ ,1982.

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Classification of ECG Patterns Using Fuzzy Rules Derived from ID3-Induced Decision Trees.pdf

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