ECG SIGNAL CLASSIFICATION USING SUPPORT VECTORMACHINE BASED ONWAVELET MULTIRESOLUTION ANALYSIS

Ayman Rabee and Imad Barhumi

Faculty of EngineeringUnited Arab Emirates UniversityP.O.Box 17555, Al-Ain, UAE

ABSTRACT

In this paper we propose a highly reliable ECG analysisand classification approach using discrete wavelet trans-form multiresolution analysis and support vector machine(SVM). This approach is composed of three stages, includ-ing ECG signal preprocessing, feature selection, and clas-sification of ECG beats. Wavelet transform is used forsignal preprocessing, denoising, and for extracting the co-efficients of the transform as features of each ECG beatwhich are employed as inputs to the classifier. SVM isused to construct a classifier to categorize the input ECGbeat into one of 14 classes. In this work, 17260 ECG beats,including 14 different beat types, were selected from theMIT/BIH arrhythmia database. The average accuracy ofclassification for recognition of the 14 heart beat types is99.2%.

1. INTRODUCTION

The Electrocardiogram is a graphical representation of theheart electrical activity which is commonly used for diag-nosing many heart diseases. ECG signals are composedof P wave, QRS complex, and T wave. Diagnosing a beatof the heart is based on investigating the shape, the dura-tion, and the relationship between these waves. However,interpretation of these waves or the heart state is not aneasy task. In fact, the ECG signal is non-stationary, andthus, symptoms of a disease, if any, may not occur regu-larly. Therefore, Physicians used to record and monitorthe heart for a long time before they are able to classifythe rhythm into a well-known type. In addition, the sizeof the generated data can be huge which requires a lot oftime and effort, which necessitates the need for an auto-matic classification system.In automatic ECG classification system, it is essen-

tial to accurately classify the ECG beat. In such systemsthe performance depends on the quality of the signal, theclassification algorithm, and the training and testing datasets. Over the past few years, many researchers developedand utilized different techniques and algorithms to clas-sify ECG heartbeats, for example neural networks [1, 2],kth nearest neighbors [3, 4], and support vector machine(SVM) [5, 6] which becomes recently a popular classifica-tion method.This paper proposes classification of 14 different ECG

heartbeats based on SVM and discrete wavelet transform(DWT). The DWT is utilized to serve two purposes. First,to remove the baseline wandering and to enhance the ECGsignal using denoising. Second, the DWT coefficients areused to form the feature vectors as input to the SVM classi-fier. The 14 different ECG heartbeats were extracted fromthe MIT/BIH arrhythmia database [7].This rest of the paper is organized as follows. Sec-

tion 2 presents ECG signal preprocessing including a briefdescription of DWT. Section 3 introduces SVM classifi-cation methodology for ECG signals. In section 4 wepresent our experimental results and discussion.

2. ECG DATA PREPROCESSING

All raw ECG signals used in this work are obtained fromrecords of the MIT/BIH arrhythmia database [7] that con-tains different heartbeat types. Each record was sampledat a sampling frequency of 360Hz for a duration of 30 min-utes. These data samples are often corrupted by noise thatcan interfere with the frequencies of the P-QRS-T waves.In order to obtain reliable and useful information from theECG signal, we need to process the ECG signal before ap-plying any feature extraction method. Baseline wanderingdue to body movement and noise are major sources of dis-tortion in ECG signal classification. In this sense, wavelettransform (WT) is used to remove the baseline wanderingand denoise the signal to remove other types of noise.

2.1. Wavelet Transform

WT is useful in analyzing transient and time-varying sig-nals. Hence, it is of great use in the analysis of ECG sig-nals. In general, for a signal f(t), the WT is defined asthe convolution of the signal with a scaled version of themother wavelet function ψ(t). The scale sWT can be ex-pressed as

Wsf(t) = f(t)∗ψs(t) =1

s

∫ ∞

−∞f(τ)ψs(

t− τ

s)dτ, (1)

where ψs(t) is the dilation of the mother wavelet functionψ(t) by the scale parameter s, which can be expressed as

ψs(t) =1

sψ(t

s). (2)

The 11th International Conference on Information Sciences, Signal Processing and their Applications: Special Sessions

978-1-4673-0382-8/12/$31.00 ©2012 IEEE 1319

If the scale parameter s is sampled along the dyadic se-quence 2j , j = 1, 2 . . ., the WT is called dyadic WT [8, 9].Therefore, the dyadic WT can be written as

W2jf(t) =1√2j

∫ ∞

−∞f(τ)ψ√

2j(t− τ√

2j)dτ. (3)

For digital signals, the discrete WT (DWT) is appliedas follows. The dyadic DWT for a digital signal f(n) isobtained fromMallat algorithm according to the following

S2jf(n) =∑k∈Z

hkS2j−1f(n− 2j−1)k, (4)

W2jf(n) =∑k∈Z

gkS2j−1f(n− 2j−1)k, (5)

where S2j is a smoothing function, S2jf(n) is the ap-proximation of the original signal (low frequency coeffi-cients), W2jf(n) is the detail of the original signal (highfrequency coefficients), and hk and gk are the coefficientsof the lowpass and highpass filters, respectively.Therefore, the DWT decomposes the signal into de-

tail signal and approximation signal. The selection of thewavelet function and the number of decomposition levelsis crucial in our analysis. In fact, the selection of theseparameters is based on the type of the application and thefrequency content. Thus, the best wavelet for our work isfound to be Daubechies wavelet family, which is similarin shape to ECG signals. Experimenting with Daubechieswavelet, it was found that the best efficiency is obtainedwhen Daubechies wavelet of order 4 is used. As men-tioned earlier, the use of DWT is of two folds. In one hand,the DWT is used for baseline wandering removal and de-noising, and for the other hand, the DWT coefficients areused as input vectors for the SVM classifier as explainednext.

2.2. Baseline Removal and Denoising

Baseline wander mainly comes from respiration and mo-tion with frequency content that is between 0.15Hz and0.3Hz. In this work we utilized WT to eliminate this un-desired interference. Wavelet transform is found to be bet-ter than other methods to remove this type of error sinceit introduces no latency and results in less distortion. Inaddition, decomposing the signal using WT into signalsapproximation, which are high scale low frequency com-ponents, and signals detail, which are low scale high fre-quency components, can help to distinguish between thedesired content and the noise content. To this end, to findthe level at which the baseline drift is mainly concentrated,Using the WT, the ECG signal is decomposed into levelsfrom 1 to 8 as depicted in Fig. 1 and Fig. 2. It is observedthat the baseline drift is most serious at scale 8. Therefore,the approximation at scale 8 is chosen to eliminate thisdrift. In this sense, the scale 8 is used to reconstruct thebaseline wandering, and the reconstructed signal is sub-tracted from the original signal as shown in Fig. 3.WT can also be used to remove other types of noise in

ECG signals following the same principle as before. Us-ing WT the signal is decomposed into several levels, the

wavelet coefficients are then modified by applying a cer-tain thresholding. The adopted threshold in this work isthe minimax thresholding proposed in [10].

0 2000 4000 6000 8000 10000 12000−0.5

0

0.5

(a)

0 2000 4000 6000 8000 10000 12000−0.5

0

0.5

(b)

0 2000 4000 6000 8000 10000 12000−0.5

0

0.5

(c)

0 2000 4000 6000 8000 10000 12000−1

0

1

(d)

Fig. 1. Wavelet decompositions (a) - (d) for levels 8 to 5,respectively

0 2000 4000 6000 8000 10000 12000−1

01

(e)

0 2000 4000 6000 8000 10000 12000−0.5

00.5

(f)

0 2000 4000 6000 8000 10000 12000−0.2

00.2

(g)

0 2000 4000 6000 8000 10000 12000−0.2

00.2

(h)

Fig. 2. Wavelet decompositions (e) - (h) for levels 4 to 1,respectively

2.3. Feature Extraction

In this step, the data is sampled such that each sample con-tains one heartbeat which is composed of P-QRS-T waves.To capture these waves, annotation files from MIT/BIHwere used to allocate the position of the R waves. Thena vector of 251 data points is formed by taking 90 datapoints to the left of the R wave and another 160 data pointsto the right of the R wave, and thus covering all partsof each beat. Fig. 4 shows typical ECG heartbeats of14 different types extracted from the MIT-BIH database.The 14 different beat types used in this work are normalbeat (NB), left bundle branch block beat (LBBB), rightbundle branch block beat(RBBB), aberrated atrial prema-ture (AAPC), premature ventricular contraction (PVC), fu-sion of ventricular and normal beat (FOV), nodal (junc-tional) premature beat (NPC), atrial premature contraction

1320

0 2000 4000 6000 8000 10000 12000−2

0

2

sample point (a)

Am

plitu

de

0 2000 4000 6000 8000 10000 12000−1

0

1

sample point (b)

Am

plitu

de

0 2000 4000 6000 8000 10000 12000−2

0

2

sample point (c)

Am

plitu

de

Fig. 3. Removal of baseline wander (a) Raw ECG signal.(b) Baseline. (c) Baseline-free ECG signal

(APC), ventricular escape beat (VES), nodal escape beat(NES), pace beat (PACE),ventricular flutter wave (VFW),atrial escape beat (AES), and Fusion of paced and normalbeat (FVN).As concluded from the previous section, and accord-

ing to [11], the frequency content of the QRS complex ismainly concentrated at detail levels 3, 4 and 5. Also it isclear that the frequency spectrum of the T and P wavesare mainly concentrated at detail levels 6 and 7. While allother levels (higher and lower) may contain uneliminatednoise types, and thus are excluded. Therefore, for eachheartbeat the detail signals D3 to D7 are chosen to formthe feature vector.

0 50 100 150 200 250 300−2.5

−2

−1.5

−1

−0.5

0

0.5

1

1.5

2

2.5

sample point

EC

G S

igna

l

NBLBBBRBBBAAPCPVCFOVNPCAPCVESNESPACEVFWAESFPN

Fig. 4. ECG heartbeats of 14 types

3. SUPPORT VECTOR MACHINE

In support vector machines (SVMs) [12], the basic ideais to find the optimal hyperplanes between data points ofdifferent classes. For a given training data (xi, yi) fori = 1 . . . N , the optimization problem for the SVM is

formulated as follows.

minw,ζ,b

J(w, ζ) =1

2wTw + C

N∑i=1

ζi, (6)

such that

yi(wTϕ(xi) + b) ≥ 1− ζi, i = 1, . . . , N (7)

ζi ≥ 0, i = 1, . . . , N, (8)

where C is a positive regularization constant which is cho-sen empirically,w is the weight vector for training parame-ters, ζi is a positive slack variable indicates the distance ofxi with respect to the decision boundary, and ϕ is a nonlin-ear mapping function used to map input data point xi intoa higher dimensional space. SVMs can be written usingLagrange multipliers α ≥ 0 for (7). The solution for theLagrange multipliers is obtained by solving a quadraticprogramming problem. The SVM decision function canbe expressed as

g(x) =∑

xi∈SV

αiyiK(x, xi) + b (9)

whereK(x, xi) is the kernel function and defined as

K(x, xi) = ϕ(x)Tϕ(xi) (10)

In this work, the Gaussian radial basis function [13] isused. It is defined asK(x, z) = e−γ||x−z||2 , and the SVMclassifier was implemented using LIBSVM [14], which isone-against-one multi-class classifier.

4. RESULTS AND DISCUSSION

A total of 17260 heartbeats corresponding to 14 ECG beattypes are obtained from theMIT-BIH arrhythmia database.The data set is divided randomly into two mutually exclu-sive sets. One set is used for training the SVM classifierand the other set is used for testing the performance of theclassifier. Table 1 summarizes the type, the number andthe MIT/BIH record of each heartbeat used in this work.The kernel function we employed in this work is the

Gaussian radial basis function which can be tuned usinga single parameter γ. To get the best performance of theSVM classifier, the optimal combination of the regular-ization constant C and γ should be found. Therefore, agrid search with exponentially growing sequences was per-formed. Each combination is checked using cross valida-tion. Then the parameters with the best cross validationaccuracy are selected. In this study, C and γ are foundas C = 16 and γ = 0.0508. Using these two values, weran the SVM classifier 10 times, each time randomly di-viding the data into two mutually exclusive training andtesting sets. The results were averaged. The flowchart ofthe proposed method is shown in Fig. 5.In order to evaluate the performance of the classifica-

tion system, we used overall accuracy, sensitivity Se andspecificity Sp, which are respectively defined as

Se =TP

TP + FN(11)

1321

Beat Type # of Training Beats # of Testing Beats MIT/BIH Data File

NB 1119 1118 100LBBB 1245 1245 109RBBB 1082 1082 118AAPC 56 56 201, 202PVC 1322 1321 200, 208, 233FOV 366 365 208, 213NPC 38 37 124, 234APC 1065 1064 209, 220, 222, 223, 232VES 52 51 207NES 109 109 201, 222PACE 1808 1808 107, 217VFW 235 235 207AES 7 7 223FPN 129 129 217Total 8633 8627

Table 1. ECG Samples and records used for training and testing

��� ��� ���� ��� ����������

��� ���� ������� �������

�������!��������

��""�������������

#���$�� %���#� ������"�����

�%& &$�������������"���

�%& �������� '���������"��� �����

������"��������$���!*�!+

!���������������������

Fig. 5. Flowchart of the proposed method

Sp =TN

TN + FP(12)

where TP is the number of true positive recognized beats,TN is the number of true negative recognized beats, FPis the number of false positive recognized beats, and FNis the number of false negative recognized beats. The over-all average detection rate is defined as the percentage ofrecognized beats to the total number of tested beats.Table (2) summarizes the specificity which is for the

normal beat(NB), sensitivity of all other beat types, andthe overall accuracy. The accuracy of recognition of nor-mal beats is 100%, and thus it can be completely recog-nized from all other beat types. The recognition rate ofall other beat types are high except for atrial escape beat(AES). This low rate occured because the number of train-ing beats of this beat type is small (7 beats for trainingand another 7 beats for testing) compared to the other beattypes. In fact, record number 223 is the only record inthe MIT/BIH arrhythmia database that contains this typeof arryithmia and it has few number of beats of this kind.The overall recognition rate of different heartbeats is highand reaches 99.2%.Additionally, our results have been compared with other

ECG beat classification systems, fuzzy hybrid neural net-works [15], Independent Component Analysis with SVM[5], wavelet transform multiresolution analysis with neu-

ral network [16], Fourier Transform neural networks [17],and discrete wavelet transform [18]. Table (3) lists therecognition rate of these methods along with the proposedscheme. It is clear that the proposed method in this workoutperforms other methods in terms of recognition rate.

5. CONCLUSION

This study shows that building a classification system us-ing SVM based on discrete wavelet transform multireso-lution analysis, can achieve high classification accuracythat reaches 99.2% overall average detection rate. Thewavelet transform multiresolution analysis based on selec-tive coefficients is utilized to filter out different types ofnoise, extract and distinguish between desired features ofthe signal and undesired components. The extracted fea-tures are reliable, robust to noise, and classifiable. Theyare used successfully to classify 14 different heartbeats.SVM with Gaussian kernel were employed to constructthe classifier. Besides other factors, the performance ofthe classifier used depends on the optimal choice of theregularization conastant C and the kernel parameter γ. Inthe experiment, grid seach along with cross-validation areused to find the values of these two parameters.

1322

Beat Type %

SPECIFITYNB 100

SENSITIVITYLBBB 99.920RBBB 100AAPC 98.246PVC 99.398FOV 99.185NPC 97.368APC 99.069VES 98.077NES 95.614PACE 100VFW 98.326AES 63.636FPN 98.473

Overall Accuracy 99.168

Table 2. ECG Samples and records used for training andtesting

Method Number of beats Accuracy(%)

Proposed 14 99.17Osowski [15] 7 96.06Kuan [5] 8 98.70Prasad [16] 13 96.79Minami [17] 3 98.00Dokur [18] 10 97.00

Table 3. Comparison with other classifiers

6. REFERENCES

[1] G. Herrero, A. Gotchev, I. Christov and K. Egiazar-ian, “Heartbeat classification using independent com-ponent analysis and matching pursuits,” IEEE Inter-national Conference Acoustic. Speech and Signal Pro-cessing, 2005.

[2] R. Ghongade and A. Ghatol, “An Effective FeatureSet for ECG Pattern Classification,” Lecture Notes inComputer Science, Vol.4901, pp.25-32, 2007.

[3] I. Christov, I. Jekova, and G. Bortolan,“Prematureventricular contraction classification by the Kthnearest-neighbours rule,” Institute of Physics Publish-ing, Physiological Measurement, Vol.26, pp.123-130,2005.

[4] I. Christov, G. Herrero, V. Krasteva, I. Jekova, A.Gotchev and K. Egiazarian,“Comparative study ofmorphological and time-frequency ECG descriptorsfor heartbeat classification,” Medical Engineering &Physics, 2006.

[5] K.-T. Chou and S.-N. Yu,“Categorizing Heartbeats byIndependent Component Analysis and Support Vector

Machines, ”Eighth International Conference on Intel-ligent Systems Design and Applications, isda, Vol.1,pp.599-602, 2008.

[6] E. Zellmer, F. Shang and H. Zhang,“Highly Accu-rate ECG Beat Classification Based on ContinuousWavelet Transformation and Multiple Support VectorMachine Classifiers,”BMEI’, pp.1-5, 2009.

[7] Physiobank Archieve Index, MITBIH ArrhytmiaDatabase: http://www.physionet.org/physiobank/database/mitdb/

[8] S. Mallat,“Zero Crossing of a wavelet Transform,”IEEE Trans. Inform. Theory, Vol.37, pp.1019-1033,1991.

[9] C. Li, C. Zheng and C. Tai,“Detection of ECG charac-teristic points using wavelet transforms,” IEEE Trans.Biomed. Eng., Vol.42 , No.1, pp.21-8, 1995.

[10] S. Sardy, “Minimax threshold for denoising complexsignals with Waveshrink,” IEEE Transactions on Sig-nal Processing, Vol.48, pp.1023-1028, 2000.

[11] NV. Thakor, JG. Webster, and WJ. Tomkins, “Esti-mation of QRS Complex Power Spectra for Designof a QRS Filter,” IEEE Transactions on BiomedicalEngineering, Vol.31, pp.702-706, 1984.

[12] Christopher J. C. Burges,“A tutorial on support vec-tor machines for pattern recognition,” Data Miningand Knowledge Discovery, Vol. 2, pp.121-167, 1998.

[13] S. Keerthi and C. Lin, “Asymptotic Behaviors ofSupport Vector Machines with Gaussian Kernel,”Neu-ral Computation, Vol.15(7), pp.1667-1689, 2003.

[14] C. Chang and C. Lin, “LIBSVM: a Library for Sup-port Vector Machines,”2001.

[15] S. Osowski and T. Linh, “ECG beat recognition us-ing fuzzy hybrid neural network,” IEEE Transactionson Biomedical Engineering, Vol.48, pp.1265-1271,2001.

[16] G. Prasad and J. Sahambi, “Classification of ECG ar-rhythmias using Multi-Resolution Analysis and Neu-ral Networks,”Proc. IEEE Conf. on Convergent Tech-nologies(TECON 2003), Vol.1, pp.227-231, 2003.

[17] K. Minami, H. Nakajima and T. Toyoshima, “Real-time discrimination of ventricular tachyarrhythmiawith Fourier-transform neural network,” EEE Trans-actions on Biomedical Engineering, Vol.46, no.2,pp.179-185, 1999.

[18] Z. Dokur, T. Olmez and E. Yazgan, “Comparison ofdiscrete wavelet and Fourier transforms for ECG beatclassification,” in Electronics Letters, Vol.35, no.18,pp.1502-1504, 1999.

1323