referŒncias bibliogrÆficas. · 99 sepÚlveda s.; camps valls g.; soria olivas e.; sanz s salcedo;...

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LAGUNA P.; VIGO D.; JANE R.; CAMINAL P. Automatic Wave Onset and Offset Determination in ECG Signals: Validation with the CSE Database. This work is supported by grant TIC 1037-91; from CICYT (Spain), and NATO grant CGR 900058. IEEE (0276-6547/92), 1992.

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SIMON .Y. FOO, G. STUART, B. HARVEY, A. MEYER-BAESE, Neural network-based EKG pattern recognition. Engineering Applications of Artificial Intelligence 15 (3-4) (2002) pp. 253-260

STRANG, G.; NGUYEN, T. Wavelets and Filter Banks. Wellesley-Cambridge Press; Wellesley, 1996.

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WOLF A.; HALL BARBOSA C.; COSTA MONTEIRO E.; VELLASCO M.. Multiple MLP Neural Networks Applied on the Determination of Segment Limits in ECG Signals. International Work Conference on Artificial and Natural Neural Networks (IWANN), Espanha, 2003.

WOLF A.; HALL BARBOSA C.; COSTA MONTEIRO E.; VELLASCO M.; PACHECO M. A.. Determination of Segment Limits in ECG Signals Using MLP Neural Networks, pp., 366--369. Kaynak, E Alpaydin, E Oja, L Xu (Eds.) Bogazici University Press, Istanbul Turkey, 2003.

WOLF A.; HALL BARBOSA C.; COSTA MONTEIRO E.; VELLASCO M.; PACHECO M. A. Localização das ondas componentes de sinais eletrocardiográficos usando redes neurais artificiais. SBAI, São Paulo, 2003.

YANG WANG; YI-SHENG ZHU; NITISH V. THAKOR; YU-HONG XU. A Short-Time Multifractal Approach for Arrhythmia Detection Based on Fuzzy Neural Network. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol. 48 Nº 9. September 2001.


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Anexo I: Classificador Patológico (Listagem do Programa em Matlab)

function Info = ExamePatologico( FC, DV, DuracaoP, AmplitudeP, DuracaoQRS, AmplitudeR, ... AmplitudeS, DuracaoT, AmplitudeT, DuracaoPR, DuracaoQT, ... AmplitudePR, AmplitudeST, AmplitudeQ, AmplitudeJ ); FCInf = 60; FCSup = 100; PDInf = 50; PDSup = 120; PAInf = 0.03; PASup = 0.20; PRDInf = 120; PRDSup = 220; ISOInf = -0.05; ISOSup = 0.05; QRSDInf = 50; QRSDSup = 100; QRSAInf = 0.37; QRSASup = 1.6; RAInf = 0.2; RASup = 1.6; QAInf = 0; QASup = 1.6; SAInf = 0; SASup = 0.37; QTDInf = 100; QTDSup = 440; TAInf = 0.1; TASup = 0.2; TDInf = 0; TDSup = 300; JAInf = -1; JASup = 1; Mens = ''; if FC >= FCInf & FC = PDInf & DuracaoP = PAInf & AmplitudeP = QRSDInf & DuracaoQRS = RAInf & AmplitudeR = SAInf & AmplitudeS = TDInf & DuracaoT = TAInf & AmplitudeT = PRDInf & DuracaoPR = QTDInf & DuracaoQT = ISOInf & AmplitudePR = ISOInf & AmplitudeST = QAInf & AmplitudeQ = JAInf & AmplitudeJ FCSup Mens = strvcat( Mens, 'Taquicardia.' ); end;


101

if FC < FCInf Mens = strvcat( Mens, 'Bradicardia.' ); end; if AmplitudeP < PAInf Mens = strvcat( Mens, 'Amplitude da Onda P inferior aos Limites de Normalidade.' ); end; if AmplitudeP > PASup Mens = strvcat( Mens, 'Amplitude da Onda P superior aos Limites de Normalidade.' ); Mens = strvcat( Mens, 'Possível Hipertrofia Atrial.' ); end; if DuracaoP < PDInf Mens = strvcat( Mens, 'Duração da Onda P inferior aos Limites de Normalidade.' ); end; if DuracaoP > PDSup Mens = strvcat( Mens, 'Duração da Onda P superior aos Limites de Normalidade.' ); end; if DuracaoPR < PRDInf Mens = strvcat( Mens, 'Duração do Intervalo PR inferior aos Limites de Normalidade.' ); if DuracaoQRS > QRSDSup Mens = strvcat( Mens, 'Possível Síndrome de Wolf-Parkinson-White.' ); end; end; if DuracaoPR > PRDSup Mens = strvcat( Mens, 'Duração do Intervalo PR superior aos Limites de Normalidade.' ); end; if DuracaoQRS < QRSDInf Mens = strvcat( Mens, 'Duração do Complexo QRS inferior aos Limites de Normalidade.' ); end; if AmplitudeQ > QASup Mens = strvcat( Mens, 'Amplitude da Onda Q do Complexo QRS superior aos Limites de Normalidade.' ); Mens = strvcat( Mens, 'Atenção: Possível sinal de Infarto do Miocárdio.' ); end; if AmplitudeR < RAInf Mens = strvcat( Mens, 'Amplitude da Onda R do Complexo QRS inferior aos Limites de Normalidade.' ); end; if AmplitudeR > RASup Mens = strvcat( Mens, 'Amplitude da Onda R do Complexo QRS superior aos Limites de Normalidade.' ); end; if AmplitudeS < SAInf Mens = strvcat( Mens, 'Amplitude da Onda S do Complexo QRS inferior aos Limites de Normalidade.' ); end; if AmplitudeS > SASup Mens = strvcat( Mens, 'Amplitude da Onda S do Complexo QRS superior aos Limites de Normalidade.' );


102

end; if DuracaoQRS > QRSDSup Mens = strvcat( Mens, 'Duração do Complexo QRS superior aos Limites de Normalidade.' ); end; if AmplitudeST < ISOInf Mens = strvcat( Mens, 'Infra-desnível do Segmento ST.' ); end; if AmplitudeST > ISOSup Mens = strvcat( Mens, 'Supra-desnível do Segmento ST.' ); end; if DuracaoQT < QTDInf Mens = strvcat( Mens, 'Duraçao do Intervalo QT inferior aos Limites de Normalidade.' ); end; if DuracaoQT > QTDSup Mens = strvcat( Mens, 'Duraçao do Intervalo QT superior aos Limites de Normalidade.' ); end; if AmplitudeT < TAInf if AmplitudeT >= 0 Mens = strvcat( Mens, 'Amplitude da Onda T inferior aos Limites de Normalidade.' ); else Mens = strvcat( Mens, 'Onda T Negativa.' ); Mens = strvcat( Mens, 'Possível sinal de Insquemia Miocárdica.' ); end; end; if AmplitudeT > TASup Mens = strvcat( Mens, 'Amplitude da Onda T superior aos Limites de Normalidade.' ); end; if DuracaoT < TDInf Mens = strvcat( Mens, 'Duração da Onda T inferior aos Limites de Normalidade.' ); end; if DuracaoT > TDSup Mens = strvcat( Mens, 'Duração da Onda T superior aos Limites de Normalidade.' ); end; end; Info = Mens;


103

Anexo II: Artigos Relacionados à Dissertação

A. WOLF; C. HALL BARBOSA; E. COSTA MONTEIRO; M. VELLASCO; M. A. PACHECO. Determination of Segment Limits in ECG Signals Using MLP Neural Networks, pp., 366--369. Kaynak, E Alpaydin, E Oja, L Xu (Eds.) Bogazici University Press, Istanbul, Turkey, 2003.

A. WOLF; C. HALL BARBOSA; E. COSTA MONTEIRO; M. VELLASCO. Multiple MLP Neural Networks Applied on the Determination of Segment Limits in ECG Signals. International Work Conference on Artificial and Natural Neural Networks (IWANN), Espanha, 2003.

A. WOLF; C. HALL BARBOSA; E. COSTA MONTEIRO; M. VELLASCO; M. A. PACHECO. Localização das ondas componentes de sinais eletrocardiográficos usando redes neurais artificiais. Simpósio Brasileiro de Automação Inteligente (SBAI), Bauru, 2003.

LAGUNA P.; VIGO D.; JANE R.; CAMINAL P. Automatic Wave Onset and Offset Determination in ECG Signals: Validation with the CSE Database. This work is supported by grant TIC 1037-91; from CICYT (Spain), and NATO grant CGR 900058. IEEE (0276-6547/92), 1992.


'(7(50,1$7,21�2)�6(*0(17�/,0,76�,1(&*�6,*1$/6�86,1*�0/3�1(85$/�1(7:25.6

$��:ROI1��&��+DOO�%DUERVD1��(��&RVWD�0RQWHLUR2��0��9HOODVFR1��0��$��3DFKHFR11Department of Electrical Engineering and 2Department of Metrology

Pontifícia Universidade Católica do Rio de Janeiro, BrazilE-mails: [wolf, hall, marley, marco]@ele.puc-rio.br, [email protected]

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The electrocardiogram (ECG) is a one-dimensional signalwith a characteristic morphology composed by variouswaves, each one corresponding to the activity in a specificregion of the human heart. These waves have expectedranges of duration and amplitude depending on thepatient’s gender, and large deviations from such valuesindicate a series of heart diseases. This work proposes analgorithm, based on multi-layer perceptron (MLP) neuralnetworks, to automatically determine the onset and offsetof each component wave, as a first step for implementing afully automated diagnosis system. Data obtained from theMIT-BIH database have been used, comprising a series oflong term measurements in patients and also manualdefinition of the limit points performed by clinicalphysicians. The results clearly show the applicability ofthe MLP model in this biomedical task.

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An electrocardiograph is a noninvasive diagnostic toolthat records the electrical activity of the heart, presentlybeing the most widely used biomedical instrument for theinvestigation of heart diseases [1]. Ten electrodes areconnected to the patient (four electrodes connected to thelimbs, and six electrodes attached to the chest) and, bycombining pairs of electrodes, up to twelve standard leadscan be measured. The recorded signal is called anelectrocardiogram (ECG), being divided into six limbleads called I, II, III, avL, avR, avF, and six precordialleads V1, V2, V3, V4, V5 and V6. The limb leads can beconsidered as projections of an equivalent current dipole(that encompasses the electrical propagation in the heart)on various directions in the frontal plane, whilst theprecordial leads are projections on the transverse plane.

Fig. 1 shows a typical lead II measurement, where itcan be clearly seen that the signal is composed by variousseparate waves, each one corresponding to the propagationof the electrical activity in a specific region of the heart.

Figure 1. ECG signal measured by lead II, depicting thethree distinct wave components: P, QRS and T.

The P-wave corresponds to atrial electrical activation,the QRS complex corresponds to ventricular electricalactivation, and the T wave to ventricular electricalrecovery. Virtually any physician is able to visuallyinterpret an electrocardiogram and obtain a large amountof information about the patient’s health. Also, severalintelligent algorithms have already been applied in thisfield, mainly aiming to classify between normal andpathologic patterns, which generally is a conventionalclassification task [2-4].

Another approach to the automatic analysis anddiagnosis of ECG signals is to initially determine theamplitude and duration of the individual waves, and thencompare such values with average expected ranges, withlarge deviations indicating a series of heart diseases. Thisfirst problem, of extracting and measuring the individualwaves, has been tackled by a number of differentalgorithms, of which the wavelet transform seems to be themost promising [5,6]. However, some signal-to-noiseissues sometimes prevent correct application of wavelets.

This work presents a MLP neural network algorithm,trained using data from the MIT-BIH database [7], toautomatically determine the onset and offset of each waveor complex. Next section presents the ECG data used inthis work, followed by the pre-processing employed todefine the training patterns for the neural networks.Sections 4 and 5 present the neural networks and results,followed by a brief discussion.


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The ECG data used in this work have been obtained fromthe MIT-BIH database [7], a well-known public databaseof ECG recordings. A total of 11 long-term signals havebeen selected, with an average of 30 cardiac cycles perpatient, for which there were available also annotation datadefined by expert physicians. For each annotated cycle,the instants corresponding to the following events havebeen marked:

• P-wave onset• P-wave peak• P-wave offset• QRS-complex onset• R-wave peak• QRS-complex offset• T-wave onset• T-wave peak• T-wave offset

Thus, there are available a total of 273 cardiac cycles,each one resembling the sample shown in Fig. 1, and 273sets of time instants that define the limits and peakpositions of the various components of each cycle.However, only 103 annotations are available for the T-wave onset, due to the difficulty of defining visually suchtime instants.

From the time instants above defined, all relevant timeparameters can be determined, such as:

• Heart rate (bpm)• P-wave duration (ms)• P-wave amplitude (mV)• QRS-complex duration (ms)• R-wave amplitude (mV)• T-wave duration (ms)• T-wave amplitude (mV)• PR interval duration (ms)• QT interval duration (ms)

Next section describes the pre-processing stepsnecessary to define the neural networks input and outputtraining patterns.

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Initially, the time instants corresponding to the peaksof the P-waves, R-waves and T-waves are determined, bymeans of a simple threshold and windowing algorithm.

From the time instants corresponding to consecutiveR-wave peaks, the instantaneous heart rate can be easilycalculated. Next, a time window, herein called FDSWXUHIUDPH,�is defined, such as to extract a single cardiac cycle(PQRST sequence), as shown in Fig. 2. An heuristicalgorithm has been empirically derived in order to definethe capture frames, based on the position of the individualwave peaks, as follows.

Figure 2. Capture frame divided into three wave frames(vertical dashed lines), and annotated time instants (crosses)

as defined in the MIT-BIH database by clinical experts.

The beginning of each capture frame is defined at 90%the distance between the corresponding P-wave peak andthe preceding T-wave peak, whilst its endpoint is definedat 30% the distance between the corresponding T-wavepeak and the following P-wave peak. This way, theasymmetry of the ECG signal about the R-peak is takeninto account. Also shown in Fig. 2 are the expert-definedtime instants corresponding to the various waves.

For each capture frame, the isoelectric line (zeromilivolts, corresponding to no cardiac electrical activity) isrecovered, as the signal is generally corrupted by lowfrequency noise, mainly due to patient motion andrespiration. To do so, a linear best fit is employed,considering that the beginning and endpoint of eachcapture frame must be at zero level.

Each corrected capture frame is then subdivided intothree ZDYH� IUDPHV, corresponding to P-wave, QRS-complex and T-wave. The “P” wave frame spans from thebeginning of the capture frame to half the distancebetween the P-wave and R-wave peaks. The “QRS” waveframe starts at the endpoint of the P-wave frame and endsat half the distance between the R-wave and T-wavepeaks. Finally, the “T” wave frame spans from theendpoint of the QRS wave frame to the endpoint of thecapture frame, as depicted in Fig. 2.

The signals corresponding to each wave frame are thennormalised. Finally, the absolute value of the QRScomplex is calculated.

Then, for each wave frame, a number of percent levelsare defined (e.g. 15%, 30%, 45%, 60%, 75%), and thetime instants at which the normalised wave crosses eachlevel are determined. Also, the instant relative to eachwave peak is considered as the origin of time (W = 0), thustime instants located to the left of the peaks are negative,and the ones to the right are positive. Figures 3 through 5show examples of the procedure for each type of waveframe.

Finally, each time instant is divided by itscorresponding wave frame length, thus being normalisedto the range [-1, +1]. These normalised values are theneural network input patterns, as described in the nextsection.

P QRS T


Figure 3. P wave frame, P-wave peak position (cross) and timeinstants corresponding to the various percent levels (dots).

Figure 4. QRS wave frame, R-wave peak position (cross) andtime instants corresponding to the various percent level (dots).

Figure 5. T wave frame, T-wave peak position (cross) and timeinstants corresponding to the various percent levels (dots).

�� 0/3�1(85$/�1(7:25.6The neural system is composed by four MLP neural

networks, with one hidden layer and log sigmoidactivation functions. The first and second neural networks,called “ P” and “ QRS” networks, have 2 output neurons,yielding the onset and offset of the corresponding wave,and are trained with the instants defined by the expertannotations as output targets. The remaining neuralnetworks, denoted “ TB” and “ TE” , have one outputneuron each, and yield T wave onset and offset,respectively. Such division was necessary due to theasymmetry that is inherent to the T waves.

The total number of patterns is 273, divided in 196 fortraining, 50 for validation and 27 for test. In the case of“ TB” wave, there are 103 patterns, divided in 60 fortraining, 16 for validation and 27 for test. The number ofinput neurons is defined by the number of percent levelsused, and the optimal number can be different for eachwave. For each neural network, the number of neurons inthe hidden layer has been systematically varied, from 6 to20, in order to determine the best configuration.

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Table 1 presents the training and validation meanabsolute percent errors (MAPE) for the most relevantconfigurations tested for the “ P” neural networks. The besthidden layer size for each number of input neurons isindicated in boldface. Table 2 presents similar results forthe most relevant configurations tested for the “ QRS”neural networks.

Table 1. Results obtained for the “ P” neural network.

# neurons MAPE (%)Input Layer Hidden Layer Training Validation

10 7 23.28 34.4910 30.38 44.3912 21.28 39.5114 15.84 34.59�� 20 39.88 57.96

12 � �� 10 21.13 36.2012 16.50 35.5714 21.57 39.5618 5.33 28.8320 15.30 37.81

14 7 35.73 47.5410 23.46 36.8512 17.60 35.0914 24.18 44.52�� 20 18.39 27.43

Table 2. Results obtained for the “ QRS” neural network.


14 8 17.38 22.47�� 12 38.06 41.1814 20.80 25.6516 15.80 21.9620 19.37 25.97

16 8 25.57 30.7910 12.28 17.4112 20.66 26.1414 21.10 28.60�� 20 23.25 30.67

18 8 6.28 18.79�� 12 28.93 34.6414 25.70 32.4616 10.15 19.2720 12.78 24.10


Now, Tables 3 and 4 present the training andvalidation errors for the best configurations investigatedfor the “ TB” and “ TE” neural networks, respectively.

Table 3. Results obtained for the “ TB” neural network.


12 � �� 10 0.03 12.2612 < 0.005 10.9015 0.01 11.1417 < 0.005 10.6120 < 0.005 11.80

14 6 1.00 11.48�� < 0.005 ��12 < 0.005 11.9115 < 0.005 11.6717 < 0.005 13.0520 < 0.005 12.51

16 6 0.26 9.7010 0.03 10.2712 0.14 11.63�� 17 0.07 10.5520 0.02 11.02

Table 4. Results obtained for the “ TE” neural network.


10 6 9.65 13.30� ��

12 7.90 12.8815 7.32 12.7418 6.90 13.0720 6.63 12.18

12 � �� 9 8.06 12.59

12 7.26 12.5315 6.55 12.9618 5.50 13.1320 4.88 12.86

14 � �� 9 7.33 13.06

12 5.70 13.5115 4.81 12.9018 4.07 12.4420 3.52 12.87

The MAPE errors for the 27 test patterns using the bestconfigurations (selected based on the validation MAPEerrors) were 26.49% for the “ P” network, 32.30% for the“ QRS” network, 23.79% for the “ TB” network and 9.88%for the “ TE” network. Finally, Fig. 6 presents a test of thefour combined neural networks in a full heart cycle.

Figure 6. Test of the combined best neural networks in a fullcycle. The expert annotated time instants are shown as circles,

and the positions defined by the neural network as crosses.

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The test results clearly show the applicability of theMLP model in the biomedical task of determining theonset and offset of the various waves that compose anECG signal. Although the validation and test MAPE errorsseem quite large at first, one have to consider that theyhave been amplified by the pre-processing, as the sub-waves have been detached from the complete cycle, andthe time origin of each wave has been redefined.

When comparing the onsets and offsets with theexpert-defined positions, as shown in Fig. 6, the actualabsolute errors are much smaller, with the larger onesconcentrated in the T-wave onset positions (as expected,since there were less available training data in this case).Another source of error was the definition of the targets,which has been made visually by clinical experts, beingmuch prone to errors.

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[1] Webster, J. G.: Medical Instrumentation: Application andDesign. John Wiley & Sons, New York, NY (1998)

[2] Maglaveras, N., Stamkopoulos, T., Diamantaras, K.,Pappas, C., Strintzis, M.: ECG pattern recognition andclassification using non-linear transformations and neuralnetworks: A review. Int. J. Med. Inf. 52 (1998) 191–208

[3] Nugent, C.D., Webb, J.A.C., Black, N.D., Wright, G.T.H.,McIntyre M.: An intelligent framework for theclassification of the 12-lead ECG. Art. Intel. Medicine 16(1999) 205–222

[4] Sternickel, K.:Automatic pattern recognition in ECG timeseries. Comp. Meth. Prog. Biomedicine 68 (2002) 109–115

[5] Sivannarayna, N., Reddy, D.C.: Biorthogonal wavelettransforms for ECG parameters estimation. Med. Eng. Phys.21 (1999) 167-174

[6] Bahoura, M., Hassani, M., Hubin, M.: DSP implementationof wavelet transform for real time ECG wave formsdetection and heart rate analysis. Comp. Meth. Prog.Biomedicine 52 (1997) 35-44

[7] Laguna P., Mark R.G., Goldberger A.L., Moody G.B.: Adatabase for evaluation of algorithms for measurement ofQT and other waveform intervals in the ECG. Computers inCardiology 24 (1997) 673-676


Multiple MLP Neural Networks Applied on theDetermination of Segment Limits in ECG Signals

A. Wolf1, C. Hall Barbosa1, E. Costa Monteiro2, M. Vellasco1

1 Department of Electrical Engineering and 2Department of MetrologyPontifícia Universidade Católica do Rio de Janeiro

Rua Marquês de São Vicente, 225, Rio de Janeiro, 22453-900 RJ, Brazil{wolf, hall, marley}@ele.puc-rio.br

Abstract. The electrocardiogram (ECG) has a characteristic morphology com-posed by various waves, corresponding to the activities in different regions ofthe human heart. These waves have expected ranges of duration and amplitude,and large deviations from such values indicate a series of heart diseases. Thiswork proposes an algorithm, based on multiple multi-layer perceptron (MLP)neural networks, to automatically determine the onset and offset of each com-ponent wave, as a first step for implementing a fully automated diagnosis sys-tem. Data obtained from the MIT-BIH database have been used, comprising aseries of long term measurements in patients and also manual definition of thelimit points performed by clinical physicians. The results clearly show the ap-plicability of the MLP model in this biomedical task. Also, the combination ofthe results provided by all trained neural networks, instead of only the best one,has proven to improve the overall performance of the system.

1 Introduction

An electrocardiograph is a noninvasive diagnostic tool that records the electrical ac-tivity of the heart, presently being the most widely used biomedical instrument for theinvestigation of heart diseases [1]. Ten electrodes are connected to the patient (fourelectrodes connected to the limbs, and six electrodes attached to the chest) and, bycombining pairs of electrodes, up to twelve standard leads can be measured. The re-corded signal is called an electrocardiogram (ECG), being divided into six “limb”leads called I, II, III, avL, avR and avF, and six “precordial” leads, V1 through V6.The limb leads can be considered as projections of an equivalent current dipole (thatencompasses the electrical propagation in the heart) on various directions in the frontalplane, whilst the precordial leads are projections on the transverse plane.

Fig. 1 shows a typical lead II measurement, where it can be clearly seen that thesignal is composed by various separate waves, each one corresponding to the propa-gation of the electrical activity in a specific region of the heart. The P wave corre-sponds to atrial electrical activation, the QRS complex corresponds to ventricularelectrical activation, and the T wave to ventricular electrical recovery.


Fig. 1. Lead II ECG signal, depicting the three distinct wave components: P, QRS and T.

Virtually any physician is able to interpret an electrocardiogram and obtain a largeamount of information about the patient’s health. Also, several intelligent algorithmshave already been applied in this field, mainly aiming to classify between normal andpathologic patterns, which generally is a conventional classification task [2-4].

Another approach to the automatic analysis and diagnosis of ECG signals is to ini-tially determine the amplitude and duration of the individual waves, and then comparesuch values with average expected ranges, with large deviations indicating a series ofheart diseases. This first problem, of extracting and measuring the individual waves,has been tackled by a number of different algorithms, of which the wavelet transformseems to be the most promising [5,6]. However, some signal-to-noise issues some-times prevent correct application of wavelets.

This work presents a multiple MLP neural network system, trained using data fromthe MIT-BIH database [7,8], to automatically determine the onset and offset of eachwave or complex. Next section presents the ECG data used in this work, followed bythe pre-processing that defines the training patterns for the neural networks. Sections 4and 5 present the neural networks and results, followed by a brief discussion.

2 ECG Data

The ECG data used in this work have been obtained from the MIT-BIH database [7,8],a well-known public database of ECG recordings. A total of 11 long term signals havebeen selected, with an average of 30 cardiac cycles per patient, for which there wereavailable also annotation data defined by expert physicians. For each annotated cycle,the instants corresponding to the following events have been marked:

• P wave onset, peak and offset• QRS complex onset and offset• R wave peak• T wave onset, peak and offset

There are available a total of 273 cardiac cycles, each one resembling the sampleshown in Fig. 1, and 273 sets of time instants that define the limits and peak positionsof the various components of each cycle. However, only 103 annotations are availablefor the T wave onset, due to the difficulty of defining such time instants.


From the time instants above defined, clinically relevant time and amplitude pa-rameters can be determined, such as:

• Heart rate (bpm)• P wave duration (ms) and amplitude (mV)• QRS complex duration (ms) and R wave amplitude (mV)• T wave duration (ms) and amplitude (mV)• PR interval duration (ms)• QT interval duration (ms)

Next section describes the pre-processing steps necessary to define the neural net-works input and output patterns.

3 Pre-Processing

Initially, the time instants corresponding to the peaks of the P, R and T waves aredetermined, by means of a simple threshold and windowing algorithm. From the timeinstants corresponding to consecutive R wave peaks, the instantaneous heart rate canbe easily calculated. Next, a time window, herein called capture frame, is defined suchas to extract a single cardiac cycle (PQRST sequence), as shown in Fig. 1.

A heuristic algorithm has been empirically derived in order to define the captureframes, based on the position of the individual wave peaks. The beginning of eachcapture frame is defined at 90% the distance between the corresponding P wave peakand the preceding T wave peak, whilst its endpoint is defined at 30% the distancebetween the corresponding T wave peak and the following P wave peak, as shown inFig. 2. This way, the asymmetry of the ECG signal about the R peak is taken intoaccount. Also shown in Fig. 2 are the expert-defined time instants corresponding to thevarious waves.

For each capture frame, the isoelectric line (zero milivolts, corresponding to ab-sence of cardiac electrical activity) is recovered, as the signal is generally corrupted bylow frequency noise, mainly due to patient motion and respiration. To do so, a linearbest fit is employed, considering that the beginning and endpoint of each captureframe must be at zero level.

Each capture frame is then subdivided into three wave frames, corresponding to Pwave, QRS complex and T wave. The P wave frame spans from the beginning of thecapture frame to half the distance between the P wave and R wave peaks.

Fig. 2. Capture frame (defined by the vertical solid lines) divided into three wave frames (de-fined by the vertical dashed lines), and annotated time instants (crosses) as defined in the MIT-BIH database by clinical experts.

P QRS T


The QRS wave frame starts at the endpoint of the P wave frame and ends at halfthe distance between the R and T wave peaks. Finally, the T wave frame spans fromthe endpoint of the QRS wave frame to the endpoint of the capture frame, as depictedin Fig. 2. The signals corresponding to each wave frame are then extracted and nor-malised. Finally, the absolute value of the QRS wave frame is calculated.

Then, for each wave frame, a number of percent levels is defined (e.g. 15%, 30%,45%, 60%, 75%), and the time instants at which the normalised wave crosses eachlevel are determined. Also, the instant relative to each wave peak is considered as theorigin of time (t = 0), thus time instants located to the left of the peaks are negative,and the ones to the right are positive. Figures 3 through 5 show examples of the proce-dure for the three types of wave frame.

Finally, each time instant is divided by half of its corresponding wave framelength, thus being normalised to the range [-1, +1]. These normalised values define theneural network input patterns, as described in the next section.

Fig. 3. P wave frame, P wave peak position (cross) and time instants corresponding to the vari-ous percent levels (dots).

Fig. 4. QRS wave frame, R wave peak position (cross) and time instants corresponding to thevarious percent level (dots).

Fig. 5. T wave frame, T wave peak position (cross) and time instants corresponding to thevarious percent levels (dots).


4 MLP Neural Network System and Results

The neural system is composed by four types of MLP neural networks, with one hid-den layer and log sigmoid activation functions. The first and second neural networkstypes, called “P” and “QRS” networks, have 2 output neurons, yielding the onset andoffset of the corresponding wave or complex. The remaining neural networks, denoted“TB” and “TE”, have one output neuron, yielding T wave onset and offset, respec-tively. Such division was due to the asymmetry that is inherent to the T waves.

The total number of patterns was divided in 196 for training, 50 for validation and27 for test. In the case of the “TB” network the patterns were divided in 60 for train-ing, 16 for validation and 27 for test. The number of input neurons varied from 10 to18, and the optimal number can be different for each wave. For each neural network,the number of hidden neurons has been varied from 5 to 20. For each configuration, atotal of ten neural networks have been trained, in order to take into account the ran-domness associated with synaptic weights initialisation. Tables 1 through 4 presentsthe mean absolute percent errors (MAPE) for the most relevant configurations testedfor the “P”, “QRS”, “TB” and “TE” neural networks, respectively.

Table 1. Results obtained for the “P” neural network

# Input LayerNeurons

# Hidden LayerNeurons

TrainingMAPE (%)

ValidationMAPE (%)

TestMAPE (%)

10 10 6.75 12.68 24.0515 4.55 12.58 23.84

12 5 8.99 13.01 25.6510 6.00 13.67 24.76

14 5 8.61 12.88 25.3320 0.41 13.02 24.52

16 5 8.71 13.52 26.2820 0.06 13.74 24.25

18 5 7.75 14.17 29.1320 0.00 13.56 24.42

Table 2. Results obtained for the “QRS” neural network



TrainingMAPE (%)

ValidationMAPE (%)

TestMAPE (%)

10 5 4.21 5.28 5.1720 2.36 5.24 5.36

12 10 3.30 5.56 5.2520 1.73 5.79 5.10

14 15 1.82 5.82 5.5720 1.12 6.01 5.53

16 10 2.79 5.92 5.4020 0.86 5.77 5.66

18 5 3.75 6.28 5.8615 0.96 5.84 5.91


Table 3. Results obtained for the “TB” neural network



TrainingMAPE (%)

ValidationMAPE (%)

TestMAPE (%)

10 5 5.64 7.77 13.2310 4.62 7.52 12.66

12 10 4.20 7.34 11.7620 3.16 6.89 11.80

14 5 4.88 7.09 13.16��

16 5 4.54 6.94 12.0310 3.01 6.88 12.02

18 10 2.96 7.26 11.9215 1.72 7.46 12.26

Table 4. Results obtained for the “TE” neural network



TrainingMAPE (%)

ValidationMAPE (%)

TestMAPE (%)

10 5 6.34 7.44 7.9710 5.97 7.28 8.01

12 10 4.98 7.09 8.9315 4.62 7.33 9.28

14 5 5.67 7.06 9.4915 4.14 7.15 9.29

16 15 4.00 7.13 9.3720 3.60 7.29 9.56

18 15 3.25 7.23 8.5720 2.73 7.38 9.14

The best configuration for each table, based on the validation MAPE, is indicated inboldface. The results obtained for the test patterns were satisfactory, with the largesterrors in the “P” networks (23.84%), followed by “TB” (12.68%) and “TE” (9.49%),with the best results obtained for the “QRS” network (5.36%). Such results are com-patible with the nature of the individual waves, as the P wave is generally the one withthe poorest signal-to-noise ratio.

Regard now that a total of 200 networks have been trained for each wave (5 inputlayer sizes, 4 hidden layer sizes, and 10 networks for each configuration) and are al-ready available for use. Also, the test patterns had already been presented to all neuralnetworks, yielding a vector of output values for each test pattern.

For instance, Fig. 6 presents two examples of histograms of the output values, forthe “P” neural networks, together with the arithmetic means and corresponding tar-gets. It can be noticed a dispersion of the outputs about the mean and about the target,what suggested that the combination of the values provided by more than one individ-ual network could improve the system performance.


Fig. 6. Two examples of histograms (for two distinct test patterns) composed by all trained “P”neural networks outputs. The solid vertical lines are the arithmetic means, and the verticaldashed lines are the corresponding targets.

The combination of several numbers of neural networks have been tested, includingthe best individual configurations shown in Tables 1 through 4 (that is, only one net-work for each wave), and also the combination of all available networks. Table 5presents a summary of the results, for 1, 5, 25 and 200 combined network outputs. Thebest configurations were selected based on the validation MAPE errors.

Table 5. Results obtained for the four types of neural networks, using only the best configura-tion, all trained configurations, or combinations of 5 and 25 best configurations

# NeuralNetworks

“P” Test MAPE (%)

“QRS” TestMAPE (%)

“TB” Test MAPE (%)

“TE” TestMAPE (%)

1 23.84 5.10 11.76 7.975 19.44 5.18 8.93 7.7625 19.44 5.99 11.02 12.75

200 21.62 4.85 10.72 8.68

Fig. 7 presents two sample tests of the four combinations of neural networks in fullheart cycles, using the test patterns as inputs to the five best configurations.

Fig. 7. Test of the combined best five neural networks in a full cycle. The expert annotated timeinstants are shown as circles, and the positions defined by the neural network as crosses.


5 Discussion

In Table 5, it can be noticed that, with the exception of the “QRS” network, thecombination of networks has improved the performance, as long as the number ofcombined networks is kept small. Indeed, 5 networks can be considered as an opti-mum number, having improved the “P” network MAPE by 4 percentile points and the“TB” MAPE by 3 points. If this number is further increased, to 25 and 200 networks,the performance degrades. As for the “QRS” network, the there not much differencebetween the various tests, mainly because the MAPE error is already quite small, andthus it does not benefit from the combination of various waves.

The test results clearly show the applicability of the MLP model in the biomedicaltask of determining the onset and offset of the various waves that compose an ECGsignal. When comparing the onsets and offsets with the expert-defined positions, asshown in Fig. 7, the actual absolute errors are very small, with the larger ones con-centrated in the P wave (due to poor signal-to-noise ratio) and in the T wave onset (asthere were less available training data in this case).

References

1. Webster, J. G.: Medical Instrumentation: Application and Design. John Wiley & Sons,New York, NY (1998)

2. Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C., Strintzis, M.: ECGpattern recognition and classification using non-linear transformations and neural net-works: A review. Int. J. Med. Inf. 52 (1998) 191–208

3. Nugent, C.D., Webb, J.A.C., Black, N.D., Wright, G.T.H., McIntyre M.: An intelli-gent framework for the classification of the 12-lead ECG. Art. Intel. Medicine 16(1999) 205–222

4. Sternickel, K.:Automatic pattern recognition in ECG time series. Comp. Meth. Prog.Biomedicine 68 (2002) 109–115

5. Sivannarayna, N., Reddy, D.C.: Biorthogonal wavelet transforms for ECG parametersestimation. Med. Eng. Phys. 21 (1999) 167-174

6. Bahoura, M., Hassani, M., Hubin, M.: DSP implementation of wavelet transform forreal time ECG wave forms detection and heart rate analysis. Comp. Meth. Prog. Bio-medicine 52 (1997) 35-44

7. Laguna P., Mark R.G., Goldberger A.L., Moody G.B.: A database for evaluation ofalgorithms for measurement of QT and other waveform intervals in the ECG. Com-puters in Cardiology 24 (1997) 673-676

8. Goldberger A.L., Amaral L.A.N., Glass L., Hausdorff J.M., Ivanov P.C., Mark R.G.,Mietus J.E., Moody G.B., Peng C.K., Stanley H.E.: PhysioBank, PhysioToolkit, andPhysioNet: Components of a New Research Resource for Complex Physiologic Sig-nals. Circulation 101(2000) 215-220


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5HVXPR�O eletrocardiograma, comumente chamado de ECG, éum sinal unidimensional com uma morfologia característicacomposta por diversas ondas, cada uma correspondendo àatividade elétrica de uma região específica do coração. Paraestas ondas, há faixas de valores esperados para as durações eamplitudes, as quais dependem do sexo e idade do paciente.Caso os valores encontrados sejam muito diferentes dosesperados, tem-se a indicação de uma série de possíveispatologias cardíacas. Esse trabalho propõe uma metodologiabaseada em redes neurais artificiais do tipo MLP (PXOWL�OD\HUSHUFHSWURQ), para determinar automaticamente os pontosiniciais e finais de cada onda componente do ciclo cardíaco. Osdados utilizados nos experimentos foram obtidos da base dedados pública MIT-BIH, que contém dados de pacientes comséries de longa duração, juntamente com a definição porespecialistas dos pontos limites das ondas. Os resultados dessetrabalho claramente mostram a aplicabilidade do modelo MLPna área de análise de sinais biomédicos.

3DODYUDV� &KDYHV� Redes Neurais, MLP, ECG, MIT-BIH,Biomédica.

$EVWUDFW�The electrocardiogram (ECG) is a one-dimensionalsignal with a characteristic morphology composed by variouswaves, each one corresponding to the electrical activity in aspecific region of the human heart. These waves have expectedranges of duration and amplitude depending on the patient’sage and gender, and large deviations from such values indicatea series of heart diseases. This work proposes a methodology,based on multi-layer perceptron (MLP) neural networks, toautomatically determine the onset and offset of eachcomponent wave, as a first step for implementing a fullyautomated diagnosis system. Data obtained from the MIT-BIHdatabase have been used, comprising a series of long termmeasurements in patients together with the manual definitionof the limit points performed by clinical physicians. The resultsclearly show the applicability of the MLP model in thisbiomedical task.

.H\ZRUGV� Neural Networks, MLP, ECG, MIT-BIH,Biomedical.

�� ,1752'8d2Um eletrocardiógrafo é uma ferramenta de diagnóstico nãoinvasivo que permite observar a atividade elétrica do coração,sendo esse equipamento o instrumento mais utilizado na áreamédica para a investigação de patologias cardíacas [1]. Dezeletrodos são conectados ao paciente (4 eletrodos nasextremidades dos membros, e 6 no tórax). Combinando oseletrodos em pares, são obtidas 12 derivações básicas. O sinalobtido é chamado de eletrocardiograma (ECG), sendo divididoem 6 derivações periféricas, chamadas I, II, III, avL, avR eavF, e 6 derivações precordiais, denominadas V1, V2, V3, V4,V5 e V6. As derivações periféricas podem ser consideradascomo projeções de um dipolo elétrico equivalente (querepresenta a propagação da atividade elétrica no coração) emvárias direções no plano frontal, sendo que as derivaçõesprecordiais são projeções no plano transversal.

Na Fig. 1 é apresentada uma medida utilizando a derivação II,onde pode ser visto um sinal composto das várias ondas, ondecada uma corresponde à propagação da atividade elétrica emuma região específica do coração.

A onda “P” corresponde à ativação elétrica atrial, o complexo“QRS” corresponde à ativação elétrica ventricular, e a onda“T” representa a repolarização ventricular. Um médicoespecialista é capaz de interpretar visualmente umeletrocardiograma e obter uma vasta gama de informaçõessobre a saúde cardíaca dos pacientes. Muitos algoritmosinteligentes têm sido aplicados nesse campo, principalmenteem verificação de normalidade [2-4]. Uma forma de realizardiagnóstico automático de sinais de ECG é inicialmentedeterminar a amplitude/duração das ondas e, em seguida,comparar com as médias esperadas. Caso haja grandes desvios,pode ser uma indicação de uma possível patologia cardíaca.

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Para extrair as informações das ondas, existem inúmerosmétodos já implementados, sendo que o tratamento através dewavelets é um dos métodos que apresentam resultados maispromissores [5,6]. Contudo, mesmo estes métodos apresentamproblemas quando a relação sinal-ruído é ruim.

Esse trabalho apresenta um algoritmo baseado em redes neuraisartificiais, utilizando o banco de dados do MIT [7] para definiros padrões de treinamento, com a finalidade de determinarautomaticamente os pontos iniciais e finais de cada onda oucomplexo.

Nas próximas seções serão apresentados os dados utilizadosnesse trabalho, seguidos do pré-processamento e dos métodosutilizados para o treinamento das redes neurais artificiais. Nasseções 4 e 5 são apresentados os resultados das redes neuraisartificiais, seguidos de uma breve discussão.

�� 26�'$'26�'(�(&*Os dados de ECG usados nesse trabalho foram obtidos dobanco de dados de MIT-BIH [7], que é um conhecido banco dedados de domínio público.

Um total de 11 sinais de longa duração foram selecionados,utilizando-se uma média de 30 ciclos cardíacos por paciente.Para cada sinal tem-se disponível um conjunto de “ anotações”definidas por um especialista, as quais identificam um conjuntode instantes de interesse clínico.

Para cada ciclo, os instantes marcados correspondem aosseguintes pontos:

• Ponto inicial da onda P• Pico da onda P• Ponto final da onda P• Ponto inicial do complexo QRS• Pico da onda R• Ponto final do complexo QRS• Ponto inicial da onda T• Pico da onda T• Ponto final da onda T

Foi utilizado um total de 273 ciclos cardíacos, como oapresentado na Fig. 1, e 273 conjuntos das definições doslimites e pontos centrais das ondas para cada ciclo cardíaco. Apartir dos instantes temporais acima definidos, podem serdeterminados diversos parâmetros cardíacos, como porexemplo:

• Freqüência Cardíaca (bpm)• Duração da onda P (ms)• Amplitude da onda P (mV)• Duração do complexo QRS (ms)• Amplitude da onda R (mV)• Duração da onda T (ms)• Amplitude da onda T (mV)• Duração do intervalo PR (ms)• Duração do intervalo QT (ms)

Na próxima seção é descrito o pré-processamento realizado nossinais, bem como a definição das características das redesneurais artificiais utilizadas nesse trabalho.

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�� 2�35e�352&(66$0(172Inicialmente, os instantes de tempo que correspondem aospicos das ondas P, R e T são determinados por meio dealgoritmos tradicionais de janelamento e limiar.De posse dos instantes de tempo correspondentes aos picosconsecutivos das ondas “ R” , calcula-se facilmente a freqüênciacardíaca instantânea. Em seguida, define-se uma janela, aquichamada de “ quadro de captura” , que representa um únicociclo cardíaco com todos os seus elementos da seqüência(PQRST), como mostrado na Fig. 2. Através de uma heurísticaos quadros de captura são divididos em quadros de leitura,como também indicado na Fig. 2 e descrito a seguir.

O início de cada quadro de captura é definido a 90% dadistância entre o pico da onda “ P” do ciclo atual e o pico daonda “ T” do ciclo anterior. Já o fim do quadro é definido a30% da distância entre o pico da onda “ T” do ciclo atual e opico da onda “ P” seguinte. Dessa forma, é levada emconsideração a assimetria do sinal, bem como as modificaçõesdos tamanhos dos quadros de acordo com a freqüênciacardíaca.

O sinal de ECG é geralmente corrompido por ruídos de baixafreqüência, principalmente devido à respiração. Assim, énecessário inicialmente, para cada quadro de captura, recuperara linha isoelétrica (zero milivolts, correspondente à ausência deatividade cardíaca). Para isso, um ajuste linear é implementadoconsiderando a média de alguns pontos do início e do fim doquadro de captura, deslocando todo o quadro para o nível 0.

Cada quadro de captura corrigido é então subdividido em 3quadros de leitura, correspondendo à onda “ P” , ao complexo“ QRS” e à onda “ T” .

O quadro de leitura da onda “ P” começa no início do quadro decaptura, com seu fim aproximadamente no meio da distânciaentre o pico da onda “ P” e pico da onda “ R” . O quadro deleitura do complexo “ QRS” começa no final da onda “ P” etermina na metade da distância entre o pico da onda “ R” e opico da onda “ T” . Finalmente o quadro de leitura da onda “ T”começa no final do complexo “ QRS” e termina no final doquadro de captura, como apresentado na Fig. 2.

Os sinais correspondentes a cada quadro de leitura são entãonormalizados em relação ao pico da onda correspondente, epara cada quadro de leitura um número de percentuais emrelação ao pico são definidos (ex. 15%, 35%, 45%, 60%, 75%).Em seguida, determinam-se os instantes de tempo nos quais aonda normalizada cruza cada nível percentual.

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O instante relativo ao pico de cada onda é considerado como aorigem do tempo (W� � �), sendo que os pontos localizados àesquerda do pico da onda correspondente serão consideradoscomo instantes de tempo negativos e os pontos à direita serãoinstantes de tempo positivos. As figuras 3 a 5 mostramexemplos desse procedimento para cada tipo de quadro deleitura.

Finalmente, cada instante obtido é dividido pela metade dotamanho do seu correspondente quadro de leitura, dessa formanormalizando os valores entre [-1, +1]. Estes valoresnormalizados serão utilizados como os padrões de entrada dasredes neurais artificiais, as quais serão descritas na próximaseção.

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O sistema de redes neurais é composto por 4 redes neuraisartificiais MLP, com uma camada escondida com função deativação sigmoidal. A primeira e segunda redes neuraisartificiais são chamadas de “ P” e “ QRS” , possuindo 2neurônios na camada de saída, que correspondem ao início efinal das respectivas ondas. Essas redes são treinadas,utilizando-se como objetivos os instantes de tempo definidospor médicos especialistas em análise de sinais de ECG. Asdemais redes neurais, chamadas de “ TE” e “ TD” , possuemsomente um neurônio na camada de saída, correspondendo aoponto inicial e final da onda “ T” , respectivamente. Essa divisãose fez necessária devido à grande assimetria inerente às ondas“ T” .

Para esse trabalho foram utilizados 273 padrões, divididos em196 para treinamento, 50 para validação cruzada e 27 parateste. O número de neurônios na camada de entrada é definidopelo número de valores percentuais utilizados, sendo que onúmero ótimo pode ser diferente para cada tipo de onda. Paracada rede neural artificial, o número de neurônios na camadaescondida foi sistematicamente variado de 5 a 20 neurônios, deforma a se encontrar a melhor configuração.

Para a escolha das melhores redes para cada onda, foramcriadas 800 redes, onde o número de neurônios da camada deentrada foi variado de 10 a 18, e na camada escondida de 5 a20, e treinou-se 10 redes para cada configuração, de modo aconsiderar a aleatoriedade inerente à inicialização dos pesossinápticos. Para se encontrar as melhores redes foi utilizado amétrica de erro chamada MAPE (0HDQ� $EVROXWH� 3HUFHQW(UURU), conforme definida abaixo:

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16 5 8.71 13.52 26.2820 0.06 13.74 24.25

18 5 7.75 14.17 29.1320 0.00 13.56 24.42


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# CamadaEntrada

# CamadaEscondida

MAPETreino

(%)

MAPEValidação

(%)

MAPETeste(%)

10 5 4.21 5.28 5.17��

12 10 3.30 5.56 5.2520 1.73 5.79 5.10

14 15 1.82 5.82 5.5720 1.12 6.01 5.53

16 10 2.79 5.92 5.4020 0.86 5.77 5.66

18 5 3.75 6.28 5.8615 0.96 5.84 5.91

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# CamadaEntrada

# CanadaEscondida

MAPETreino

(%)

MAPEValidação

(%)

MAPETeste(%)

10 5 5.64 7.77 13.2310 4.62 7.52 12.66

12 10 4.20 7.34 11.7620 3.16 6.89 11.80

14 5 4.88 7.09 13.16��

16 5 4.54 6.94 12.0310 3.01 6.88 12.02

18 10 2.96 7.26 11.9215 1.72 7.46 12.26

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# CanadaEntrada

# CanadaEscondida

MAPETreino

(%)

MAPEValidação

(%)

MAPETeste(%)

10 5 6.34 7.44 7.9710 5.97 7.28 8.01

12 10 4.98 7.09 8.9315 4.62 7.33 9.28

14 � �� 15 4.14 7.15 9.29

16 15 4.00 7.13 9.3720 3.60 7.29 9.56

18 15 3.25 7.23 8.5720 2.73 7.38 9.14

Os resultados obtidos são satisfatórios, com os maiores errosencontrados nas redes “ P” (23.84%), seguido por “ TE”(12.68%) e “ TD” (9.49%), com os melhores resultados obtidospor “ QRS” (5.36%). Os resultados são compatíveis com anatureza individual de cada onda, como o caso da onda P, quegeralmente enfrenta o maior problema de sinal/ruído obtendo,dessa forma, o menor desempenho das redes.

As figuras 6 e 7 apresentam os histogramas de saídas das redesneurais artificiais da onda “ P” , juntamente com a médiaaritmética (linha vertical sólida) e seu objetivo (linha verticaltracejada). Com a observação do histograma, constata-se que adistribuição dos resultados das redes neurais se concentramuito próxima ao objetivo, e portanto sugere que uma boaforma de combinação dos resultados é por meio do cálculo damédia das saídas de todas as redes treinadas.

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Os resultados das médias apresentam resultados maispromissores do que os resultados individuais. Váriascombinações de redes neurais foram testadas, sendo que natabela 5 são apresentados os resultados das melhores redesneurais artificiais, utilizando-se a combinação de 1, 5, 25 e 200redes, sendo que as mesmas foram selecionadas utilizando-se oMAPE de validação.

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Artificiais

Teste“ P”

MAPE(%)

Teste“ QRS”MAPE

(%)

Teste“ TE”

MAPE(%)

Teste“ TD”

MAPE(%)

1 23.84 5.10 11.76 7.975 19.44 5.18 8.93 7.76

25 19.44 5.99 11.02 12.75200 21.62 4.85 10.72 8.68


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(b)

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LQGLFDGDV�SHOR�VLVWHPD�QHXUDO�A figura 8 apresenta 2 exemplos de combinação de 5 redesneurais artificiais em um ciclo cardíaco.

5 Discussão

Na tabela 5, pode ser observado que, com exceção da rede“ QRS” , a combinação de redes obteve uma melhoria nodesempenho. Em testes realizados, pode-se a utilização de 5redes neurais artificiais como um número de redes bom, tendopropiciado uma diminuição de 4 pontos percentuais no MAPEda onda “ P” e 3 pontos na “ TE” . Se esse número de redes éincrementado para 25 ou 200, o desempenho é reduzido. Comoa rede “ QRS” não possui diferenças significativas durante osvários testes, principalmente no que diz respeito ao MAPE semanter pequeno, para o complexo QRS não há benefício nacombinação de várias redes.

Os testes mostram claramente a aplicabilidade de redes neuraisartificiais em modelos biomédicos para as tarefas dedeterminação dos pontos iniciais e finais das ondas quecompões os sinais cardíacos. Quando se comparam os pontosiniciais e finais com os definidos por um especialista, comomostrado na Fig. 8, o erro absoluto total é muito pequeno, e seencontra dentro dos desvios aceitos para a marcação dospontos.

Referências

[1] Webster, J. G.: Medical Instrumentation: Application andDesign. John Wiley & Sons, New York, NY (1998)

[2] Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C.,Strintzis, M.: ECG pattern recognition and classification usingnon-linear transformations and neural networks: A review. Int. J.Med. Inf. 52 (1998) 191–208

[3] Nugent, C.D., Webb, J.A.C., Black, N.D., Wright, G.T.H.,McIntyre M.: An intelligent framework for the classification ofthe 12-lead ECG. Art. Intel. Medicine 16 (1999) 205–222

[4] Sternickel, K.:Automatic pattern recognition in ECG time series.Comp. Meth. Prog. Biomedicine 68 (2002) 109–115

[5] Sivannarayna, N., Reddy, D.C.: Biorthogonal wavelet transformsfor ECG parameters estimation. Med. Eng. Phys. 21 (1999) 167-174

[6] Bahoura, M., Hassani, M., Hubin, M.: DSP implementation ofwavelet transform for real time ECG wave forms detection andheart rate analysis. Comp. Meth. Prog. Biomedicine 52 (1997)35-44

[7] Laguna P., Mark R.G., Goldberger A.L., Moody G.B.: Adatabase for evaluation of algorithms for measurement of QT andother waveform intervals in the ECG. Computers in Cardiology24 (1997) 673-676


referŒncias bibliogrÆficas. · 99 sepÚlveda s.; camps valls g.; soria olivas e.; sanz s salcedo;...

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