Cardiac Arrhythmia Detection based on Signal Variation Characteristic

Chusak Thanawattano and Surapol Tan-a-ram

National Science and Technology Development Agency (NSTDA), National Electronics and Computer Technology center (NECTEC),

Klong Luang, Pathumthani, 12120, Thailand E-mail: chusak.thanawattano@nectec.or.th and surapol.tan-a-ram@nectec.or.th

Abstract

This paper presents the classification of cardiac arrhythmia based on the signal variation characteristic of each beat type. Considered beat types including the normal beat, fusion beat and premature ventricular contraction beats are differentiated to obtain feature sets. Using the principal component analysis estimation, the detection selects the class by searching the minimal norm of the error vector obtained by basis of each type. Without the help of the timing interval information, the proposed classifier outperforms the algorithm presented in the literature. The classification accuracy of the proposed algorithm achieves perfect detection.

1. Introduction

The electrocardiogram (ECG) signal can be used

for diagnosing the cardiac conditions. The work on the

ECG detection and classification provide the option for

the physiologist to pre-screening the non-severe

arrhythmias. Moreover, it can provide the real-time

detection for 24-hour ECG monitoring of the life-

threatening conditions. Recently, there have been a

number of research groups that work on the detection

and classification of arrhythmias that cause either

severe or mild abnormal illness of cardiac condition.

Some groups conduct the research of the ECG

classification based on its characteristic or morphology

[1]. Some other research groups use the signal

transformation to extract the significant structure from

the ECG signal [2] – [4] by decomposing the ECG

signal into a set of components in feature subspace. Ge

et al. [5] use the autoregressive (AR) modeling to

extract features which are the AR coefficients from the

ECG signal.

However, as stated in [1], the classification rates of

automatic beat classifiers presented in the literature to

date have not been high enough for the classifiers to

gain wide spread clinical acceptance. This could be

caused by insufficiency of certain heart beat types used

to train the classifier. Hu et al. [6] suggests that the

classification accuracy can be boosted by using the

patient specific combined with the global classifier.

Chazal [1] suggests the use of timing interval in

combination with waveform characteristic to provide

the classification robustness. Inan et al. [2] combines

the timing interval features with the wavelet transform

of the ECG signal to be the training and testing set of

the neural-network classifier to provide more efficient

classification.

ECG analysis can be used to detect a specific

cardiac arrhythmia such as premature ventricular

contraction (PVC) or ventricular fusion beats. The

PVC results from irritated ectopic foci in the

ventricular area of the heart [2]. These foci cause

premature contractions of the ventricles that are

independent of the pace set by the sinoatrial node.

PVCs, when associated with myocardial infarction, can

be linked to mortality [7]. The fusion beats occur when

two separate pacemakers compete for control of the

ventricles [8]. Most of the time the fusion beats look

very similar to the PVC and normal beat. Chazal [1]

and Hu [6], therefore, show their classification

performances based on the data set of normal beat,

fusion beat and PVC beat.

2. Aim

Our work is to investigate the performance of the

current proposed classifier that classifies the same data

set as in [1, 6]. The ECG data set including normal

(N), fusion beat (F) and premature ventricular

contraction (P) are used for training and testing the

classifier based on the signal variation characteristic of

the ECG signal centered at the QRS complex.

2008 International Conference on BioMedical Engineering and Informatics

978-0-7695-3118-2/08 $25.00 © 2008 IEEEDOI 10.1109/BMEI.2008.294

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2008 International Conference on BioMedical Engineering and Informatics

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3. Material and methods

3.1 Data base selection

In this paper we use the three categories of beats,

including the normal beat (N), fusion beat (F) and PVC

beat (P), from MIT-BIH arrhythmias database [9] for

training and testing the proposed classifier. From the

database, in this study we select only 6 records

including 119, 200, 208, 213, 223 and 233. From these

records, only records 208 and 213 contain sufficient

number of fusion beats even though they are in small

portion compared to normal and PVC beats as shown

in Table 1. The ECG signal of each record is

segmented into 50000-sample sessions. Since each

record contain about 650000 samples, therefore a

single ECG record is divided into 13 sessions. We

randomly select the sessions 1, 3 and 5 of ECG records

200, 208 and 213 for the training phase of the

classification. The records 208 and 213 contain

sufficient numbers of fusion beats while record 200,

even though it has a very small amount of fusion beats,

is used to be a representative of PVC beats in training

phase.

Table 1. Training and testing beats from MIT-BIH database [9]

Rec N F P N F P N F P

Total Training Testing

119 1542 0 444 0 0 0 1542 0 444

200 1742 2 826 416 1 196 1326 1 630

208 1450 350 936 237 89 170 1213 261 766

213 2640 362 220 666 56 30 1974 306 190

223 2028 14 473 0 0 0 2028 14 473

233 2229 11 831 0 0 0 2229 11 831

3.2 Signal preprocessing

The ECG records are filtered by the low-pass filter

based on Pan and Tompkins [10] which has the

difference equation as in (1)

)12()6(2

)()2()(2)(

TnTxTnTx

nTxTnTyTnTynTy

���������

(1)

3.3 Feature Extraction

The low-passed ECG signals are then differentiated

to extract the signal variation characteristic based on

[10] which has the difference equation as in (2)

)]2()(2

)(2-)2()[81()(

TnTxTnTx

TnTxTnTxT/nTy

��������

(2)

While utilizing the pre-annotation of R-peak from

the MIT-BIH arrhythmia database, we segment each

ECG beat interval starting at 120th sample prior to the

R-peak to 119th sample after the R-peak. Therefore

each beat contains 240 samples centered at R-peak.

The 240-sample differentiated ECG is then

normalized so that the minimum of the signal is zero

and the maximum of the signal is one. Each 240-

sample signal is then down-sampled by a factor of

eight so that the beat interval session then has 30

samples. The 30-sample time-series sequences of each

type (N, F and P) are then formed into the matrix

format XN, XF and XP where their column vectors are

30-dimensional sequences. The dimensions of the

training matrix XN, XF and XP are then 30×1319,

30×146 and 30×396, respectively.

The principal component analysis (PCA) is then

used to obtain the new basis BN, BF and BP, of the

training matrix XN, XF and XP respectively. In this

paper we select the dimension of new basis so that the

projected time-series sequence is 3-dimensional. This

means the dimension of new basis matrix is 3×30. The

new basis is then pseudo-inverted as in (3), where B+ is

the pseudo-inverse of B and BH is the Hermitian

transpose of matrix B.

1)(�� � HH BBBB (3)

We are then obtained the pseudo-inverse matrix BN+,

BF+ and BP

+ of basis matrix BN, BF and BP,

respectively. These pseudo-inverse matrices will be

used in the detection phase that will be explained later.

3.4 Signal Detection

In the signal detection phase, a single ECG record

of unknown beat type is segmented into 240 as in the

training phase. The 240-sample sequence is then low-

passed by a low-pass filter with difference equation as

(1). The low-passed sequence is then differentiated by

(2) and down-sampled by a factor of eight. We are

now obtained the 30-dimensional testing column

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vector y that will then be classified into one of three

classes.

Let B be the basis matrix obtained by the PCA and

let B+ be its pseudo-inverse matrix, the error vector e

from the PCA estimation can then be expressed as

))(( ByBye ��� (4)

Therefore, the error vectors obtained by basis

matrices generated from training matrices XN, XF and

XP are ))(( yBBye NNN��� , ))(( yBBye FFF

���

and ))(( yBBye PPP��� respectively. The l1 norm is

used to measure the error as in (5) where ei is the ith

element of the error vector e and n is the dimension of

the error vector e.

��

�n

iie

1

1 |||||| e (5)

In this paper, the decision y is based on the lowest

l1 norm of error vectors Ne , Fe and Pe . That is

1111

1111

1111

||||)||||,||||,||||min(iffbeatPVC theis

||||)||||,||||,||||min(iffbeatFusion theis

||||)||||,||||,||||min(iffbeatNormal theis

PFPN

FFPN

NFPN

eeeey

,eeeey

,eeeey

��

�

(6)

However, the l2 norm is also possible to measure

the error but it requires more computation complexity.

4. Experiment and results

To investigate the performance of the detector, in

testing phase the rest of ECG signal from the database

including records 119, 200, 208, 213, 223 and 233, are

pre-processed. The ECG signal is low-passed by low-

pass filter with difference equation as in (1). It is then

differentiated by a system having difference equation

as in (2). The differentiated sequence is then

segmented into 240-sample vectors following with

down-sampling by a factor of eight. The 30-

dimentional vector of unknown arrhythmia is now

ready to be the input sequence of the testing process.

Fig. 1 shows the flow of the testing process. The

30-dimentional testing sequence is projected to new

space by basis BN, BF and BP. The corresponding

sequence is further projected by basis BN+, BF

+ and

BP+. The error vector obtained from each basis

projection is then measured using the l1 norm.

Figure 1. Process flow in testing phase. The basis and its pseudo-inverse matrix is obtained in training phase

0 5000 100000

10

20

N0 500

0

5

10

15

F

0 20000

10

20

P

0 5000 100000

10

20

Norm of each ECG in testing phase

0 5000

5

10

15

0 20000

10

20

0 5000 100000

10

20

0 5000

5

10

15

0 20000

10

20

Figure 2. Norm measured on error vectors obtained by basis BN (and BN

+), BF (and BF+) and BP (and BP

+), respectively.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

0

0.5

1

Classification result

N

0 100 200 300 400 500

0

0.5

1

F

0 500 1000 1500 2000 2500 3000

0

0.5

1

P

Figure 3. Result of beat type detection. The result shows the perfect detection with 100% accuracy.

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Fig. 2 shows the result of norm measured from the

error vector of each beat type. Each row represents

norm measured from error vectors of the same beat

type, N, F and P, projected to estimated space with

basis BN (and BN+), BF (and BF

+) and BP (and BP

+),

respectively. By using our proposed criteria, the basis

that obtains the minimal norm indicates the result of

the detection. Fig. 3 shows the result of the detection.

The result is indicated by one for the correct detection

and zero for misclassifying. Our method provides a

perfect detection of beat types N, F and P.

5. Discussion

The result of the classification shows that feature

selection and signal detection proposed in this paper

provide a very high accuracy in testing phase. Due to

the signal variation in time domain, we extract the

variation of each beat and then estimate using basis

prepared in the training phase. The projected

sequences are then further projected into estimation

space by their corresponding pseudo-inverse matrices.

TABLE 2. Percentage of beats of each type used in training and testing phase

Table II shows the percentage of beats used in

training and testing phase of the classification. The

result of the classification is impressive even the

training beats occupy only a small fraction of total

beats. We believe that the selection of the training beat

segment is also important.

6. Conclusion

In this paper, we discussed the proposed cardiac

beat classification of normal, fusion and premature

ventricular contraction beats. The feature used in this

paper is the signal variation in time domain. We

extract this feature by differentiation of the 240-sample

interval centered at the R-peak. Without using the

timing interval information as in [1, 2], the

performance of the classification based on the PCA

estimation achieves 100% accuracy by using training

beats fewer than 20% of each beat type.

7. Reference

[1] P. Chazal, and R. B. Reilly, “Automatic classification of

ECG betas using waveform shape and heart beat interal

features”, International Conference on Acoustics, Speech and Signal Processing (ICASSP’03), vol.2, pp.

269-272, 2003.

[2] O. T. Inan, L. Giovangrandi, and G. T. A. Kovacs,

“Robust Neural-Network Based Classification of

Premature Ventricular Contractions Using Wavelet

Transform and Timing Interval Features, IEEETransactions on Biomedical Engineering, Dec. 2006,

Vol. 53, Part 1, pp. 2507-2515.

[3] M. H. Kadbi, J. Hashemi, H. R. Mohseni, A.

Maghsoudi, "Classification of ECG Arrhythmias Based

on Statistical and Time-Frequency Features", Advances in Medical, Signal and Information Processing, 2006. MEDSIP 2006. IET 3rd Intr. Conf., July 2006, pp. 1-4.

[4] Q. Zhao, and L. Zhang, "ECG Feature Extraction and

Classification Using Wavelet,” InternationalConference on Neural Networks and Brain, 2005, (ICNN&B '05), Vol. 2, pp. 1089- 1092, 2005.

[5] D. Ge, N. Srinivasan, and S. M. Krishnan, “Cardiac

Arrhythmia Classification Using Autoregressive

Modeling”, BioMedical Engineering OnLine 2002,

http://www.biomedical-engineering-online.com

/content/1/1/5

[6] Y. H. Hu, S. Palreddy, and W. J. Tompkins, “A Patient-

Adaptable ECG Beat Classifier Using a Mixture of

Experts Approach”, IEEE Transactions on Biomedical Engineering, vol. 44, pp. 891-900, 1997.

[7] I. Atsushi, M. Hwa, A. Hassankhani, T. Liu, and S. M.

Narayan, “Abnormal Heart Rate Turbulence Predicts

the Initiative of Ventricular Arrhythmias”, Pacing Clinical Electrophysiology, vol. 11, pp. 1189-97, Nov.

28, 2005.

[8] H. J. L. Marriott, N. L. Schwartz, and H. H. Bix,

“Ventricular Fusion Beats”, Circulation, vol 26, pp.

880-884, 1962.

[9] R. Mark, and G. Moody, MIT-BIH Arrhythmia

Database [Online], Available: http://ecg.mit.edu/

dbinfo.html.

[10] J. Pan, and W. J. Tompkins, “A real-time QRS detection

algorithm”, IEEE Transactions on Biomedical Engineering, vol. 32, no. 3, pp. 230-236, 1985.

N F P N F P Training Testing

% 11.3 19.8 10.6 88.7 80.2 89.4

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