IMPROVING AN AUTOMATIC ARRHYTHMIAS RECOGNISER

BASED IN ECG SIGNALS

Jorge Corsino, Carlos M. Travieso, Jesús B. Alonso and Miguel A. Ferrer

Technological Centre for Innovation in Communications (CeTIC), University of Las Palmas de Gran Canaria

Campus de Tafira, s/n, 35017, Las Palmas de Gran Canaria, Spain

Keywords: Automatic recognition of arrhythmias, electrocardiography, neural network, principal component analysis,

wavelet transform.

Abstract: In the present work, we have developed and improved a tool for the automatic arrhythmias detection, based

on neural network with the “more-voted” algorithm. Arrhythmia Database MIT has been used in the work in

order to detect eight different states, seven are pathologies and one is normal. The unions of different blocks

and its optimization have found an improvement of success rates. In particular, we have used wavelet

transform in order to characterize the patron wave of electrocardiogram (ECG), and principal components

analysis in order to improve the discrimination of the coefficients. Finally, a neural network with more-

voted method has been applied.

1 INTRODUCTION

In Europe, cardiovascular diseases are one of most

important causes of death, with a great repercussion

in health assistance budget. For instance, to obtain

an early exact cardiovascular diagnosis is one of the

most important missions for the physicians. The

electrocardiogram is the graphic description of the

heart electric activity registered from the body

surface and is a basic element in the diagnosis of

different heart diseases.

The objective of this study is to make deeper in

the extraction of characteristics and the later

automatic classification of heart pathologies,

analyzing every aspect that takes parting.

To carry on with this objective, we have

developed Matlab software (Matlab, 2006), clear

and easy, where users have three options to practise

with all tools at their hands: making a pre-processing

with wavelet transform and in order to play with the

developed filing.

Wavelet transform (Romero-Legarreta, 2005) is

a mathematics technique that has gained importance

in the last years in all kind of applications related

with non-stationary signal process.

Although the decomposition in well defined

blocks in time and frequency, wavelet transform can

characterise the local sign regularities. This skill

allows distinguishing electrocardiogram waves

(ECG) from noise and other artefacts.

In this paper, we establish the use of

approximated wavelet coefficients taken out from

the ECG signal in order to classify eight types of

beat: normal pulse (N), extra-systole (L), premature

ventricular contraction (R), premature auricular

contraction (/), blockade left branch (A), blockade

right branch paced beat (V), fusion of normal and

paced beat (f) and fusion of normal and premature

ventricular contraction (F).

The use of principal component analysis (PCA)

(Bianchi, 2006) on the wavelet coefficients has

improved their discrimination. Finally, we have used

an automatic classification based on artificial neural

networks (NN) (Bishop, 1995), (Juang, 1992). An

improvement have been applied to NN, we have

implemented the “more voted” method, obtaining

better success rates.

2 WAVELET TRANSFORM:

FEATURE EXTRACTION

The ECG features are extracted through a pre-

processing stage in which the Wavelet transform is

applied to original ECG signal.

The Discrete Wavelet Transform (DWT) is

defined as follows:

453

Corsino J., M. Travieso C., B. Alonso J. and A. Ferrer M. (2008).

IMPROVING AN AUTOMATIC ARRHYTHMIAS RECOGNISER BASED IN ECG SIGNALS.

In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 453-457

DOI: 10.5220/0001066604530457

 SciTePress

[

]

[

]

[

]

Cjk fn n

∈

∑

(1)

where

kj,

is the transform function:

The application of different mother families on

pre-processing (artefacts elimination) and on the

feature extraction has got a set of good and

discriminate parameters.

3 PRINCIPAL COMPONENT

ANALYSIS

Principal components analysis (PCA) is a technique

used to reduce multidimensional data sets to lower

dimensions for analysis. The applications include

exploratory data analysis data and for generating

predictive models. PCA involves the computation of

the eigenvalue decomposition or Singular value

decomposition of a data set, usually after mean

centering the data for each attribute. The results of a

PCA are usually discussed in terms of scores and

loadings. This process applied to ECG arrhythmias

is named blind source separation, where there are

fewer sources than input channels.

The blind source separation consists in several

sources that are mixed in a system, these mixtures

are recorded and then they have to be separated to

obtain the estimations of the original sources. The

following figure shows the mixing system:

Figure 1: 2 Sources – 2 Mixtures system.

Generally, there are n source signals statistically

independent

)](),...,([)(

tststs

= , and m

observed mixtures that are linear and instantaneous

combinations of the previous

signals

)](),...,([)(

txtxtx

= . Beginning with the

linear case, the simplest case, we have that the

mixtures are:

(3)

Now, we need to recover s(t) from x(t). It is

necessary to estimate the inverse matrix of H, where

are contained. Once we have this matrix:

(4)

Where y(t) contains the estimations of the original

source signals, and is the inverse mixing matrix.

Now we have defined the simplest case, it is time to

explain the general case that involves convolutive

mixtures. The process is defined as follows:

Figure 2: BSS General problem.

Where is the mixing system:

(5)

The h

are FIR filters, each one represents an

acoustic transference multipath function from

source, i, to sensor, j. i and j represent the number of

sources and sensors.

4 NEURAL NETWORK

For this present work, we have implemented a

supervised classification system for the discrete

wavelet coefficients. Firstly, a neural network

classification system using time intervals obtained

from the previous extraction process is implemented.

This classifier has used a Feed-Forward Neural

Network (NN) with a Back-propagation algorithm

for training (Bishop, 1995), (Juang, 1992), where the

number of input units is given by the dimension of

the vector of features. And the number of output

units is given by the number of pathologies to

identify. Too, we have researched with different

number of neurons in the hidden layer, in order to

get the optimum recogniser.

Besides, the found success has been improved

using the method of the ‘more voted’, where we

have built a schedule with different neural networks

(see figure 1).

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nnk

ψψ

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⎤

=⋅ −

⎣

⎦

(2)

xxxx ]...[

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xxxx ]

...

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[

∑

⋅=

jiji

tshtx

)()(

)()( txWty ⋅=

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⎤

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⎣

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nnn

...

.........

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111

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

454

Figure 3: Classification System with ‘more voted’

algorithm, based on NN.

5 EXPERIMENTS

We have taken 24 signals from the MIT-BIH

ARRHYTHMIA database (MITDB)( MIT-BIH,

2007), choosing 750 samples from each class, 6000

beats to classify; some of them are recognized by the

MIT as difficult classifying signals. To remove noise

from signals, the net interferences and the base line

variations, we have use techniques proposed in (1).

It consists in obtaining detail coefficients for

different wavelet levels, to apply them a non-lineal

form threshold, using a soft-thresholding calculated

by the inverse transform to obtain the result signal.

The threshold follows this

expression:

∂

= 2log(N)

; where N is the

number of decomposition levels and coefficients

represent the details coefficients for the level to

filter. In function of the wavelet family and the

decomposition level, the result will change. In this

work, we take Daubechies 3 of level 3 following our

studies. Also we take different types of parameters

as temporal as Fourier and Wavelet coefficients.

With temporal parameters took out from our

previous works and algorithms (the time of Pwave,

PR segment, QRS complex, QT segment and T

wave, and the area of P wave, QRS complex and the

T wave) we did not get to characterize any kind of

beats, the same result were taken with Cosen Fourier

Transform (DCT).

Hence we only select the approximation wavelet

parameters like “in-parameters”. The classification

is realised with a neural networks using back-

propagation. Once took out the wavelet coefficients

with sym4 family and the third decomposition level,

the neural networks has three layers. To obtain the

number of neurons of the hidden layer, we tried with

different numbers and with 45 we got the best result

with an error of 26%. How the error is too much, we

make principal components analysis, since with it,

the network size and the computational cost are

reduced. With this study the characteristic vector is

ortogonalised to avoid the correlations of his

components, is arranged and the components with

less information are deleted. The algorithm is

applied to the characteristic vector, the mean is

established in cero and the standard deviation in one,

after, the PCA is applied, in this case with 0,02%.

The variations are showed in the next figures:

Figure 4: Signal and its coefficients.

Figure 5: Modification of the coefficients.

With this technique, the network in trained again

with the same conditions and the result with 55

neurons in the least error: 2,27%. This shows us a

satisfactory study. Many trainings are realises where

characteristics are: 3.000 beats (375 per class) for

the training stage and the same quantity for the test

stage, different PCA values (0,02%, 0,2 % and 2 %)

the second, the third and the fourth decomposition

level and ten wavelet families (Bior2.4, Bior5.5,

Bior 6.8, Harr, Sym2, Sym4, Sym5, Sym8, rBio3.1,

rBio5.5). With the result obtained we noticed is

Neural Network

‘More Voted’

Algorithm

Neural Network

IMPROVING AN AUTOMATIC ARRHYTHMIAS RECOGNISER BASED IN ECG SIGNALS

455

better have a lot of approximation coefficients and

alter make a PCA, instead of hace less quantity of

approximation coefficients, then in better a low level

and apply PCA. The best result were obtained with

the wavelet rBio 3.1 at level 2 and PCA= 0,02%

with 1,97% of error. “The most voted” technique is

applied to boot the result. This model consist of

select some networks an apply to all the same test in

parallel. Finally, the results are compared and the

result most voted is selected. In the figure 4 a double

network is represented with only two parallel

networks.

Figure 6: Parallel neural network.

With this new structure, the filing reduces error

to 1.8% in the simulation and 1.4 in the train

process. For the entire database, it has an error of

1.6%.

Table 1: Matrix confusion.

OUTPUT CLASSES

N L R / A V f F

N 375 0 0 0 0 0 0 0

L 0 372 0 0 0 0 3 0

R 1 0 372 0 2 0 0 0

/ 0 0 0 375 0 0 0 0

A 16 0 3 2 351 1 1 1

V 0 0 0 0 2 369 3 1

f 0 0 0 4 0 0 371 0

INPUT CLASSES

F 0 1 0 2 4 0 7 361

In the confusion matrix we can see, that the class

that has more errors is the premature ventricular

contraction, classifying this as normal beat, this is

because the morphology of the auricular premature

contraction is similar to the normal. Respect to the

classification between normal and pathologic signals

the filing detect the healthy signals whit a 100% and

the pathologic signal with a 99,35 % being the total

classification between this two classes a 99.7%.

Respect to the computational time, we remember

that the filing has three parts: the extractions of

wavelet characteristics, the principal components

analysis and the test process. This time are detailed

in the table 2:

Table 2: Load times in seconds.

Process Computational Time

Wavelet 0,010623 s

PCA 0,002571 s

Test 0. 111877 s

TOTAL 0,1251 s

Having in mind that a full beat has an

approximated duration of 800 ms, the filing will

classify the beat only 125 ms later, without the time

of pre-processing and segmentation. These times are

Matlab time. The part of classification depends on a

well segmentation process, this is we propose to

make a robust segmentation for noise and cardiac

pathologies.

Finally, we have compared our results with other

authors (Song, 2005), (Zimmerman, 2004),

(Jankowski, 2003). The new blocks used for this

application and with the optimization of the

remainder of the blocks, we can observe as our

results are better than the previous references.

6 CONCLUSIONS

It has been implemented and improved an automatic

arrhythmias recogniser using a neural network with

more voted algorithm. We have found error rate of

1.8% with independent samples, only using for the

test (8 different classes); and an error rate of 0.3%

for pathology or normal class.

The ECG signal used is from MIT arrhythmias

database, and it has been parameterized with DWT

coefficients and selected with PCA.

Evaluation se

eural

Network

eural

Network

voted

Decision

Random weights

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